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Haubold J, Baldini G, Parmar V, Schaarschmidt BM, Koitka S, Kroll L, van Landeghem N, Umutlu L, Forsting M, Nensa F, Hosch R. BOA: A CT-Based Body and Organ Analysis for Radiologists at the Point of Care. Invest Radiol 2024; 59:433-441. [PMID: 37994150 DOI: 10.1097/rli.0000000000001040] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2023]
Abstract
PURPOSE The study aimed to develop the open-source body and organ analysis (BOA), a comprehensive computed tomography (CT) image segmentation algorithm with a focus on workflow integration. METHODS The BOA combines 2 segmentation algorithms: body composition analysis (BCA) and TotalSegmentator. The BCA was trained with the nnU-Net framework using a dataset including 300 CT examinations. The CTs were manually annotated with 11 semantic body regions: subcutaneous tissue, muscle, bone, abdominal cavity, thoracic cavity, glands, mediastinum, pericardium, breast implant, brain, and spinal cord. The models were trained using 5-fold cross-validation, and at inference time, an ensemble was used. Afterward, the segmentation efficiency was evaluated on a separate test set comprising 60 CT scans. In a postprocessing step, a tissue segmentation (muscle, subcutaneous adipose tissue, visceral adipose tissue, intermuscular adipose tissue, epicardial adipose tissue, and paracardial adipose tissue) is created by subclassifying the body regions. The BOA combines this algorithm and the open-source segmentation software TotalSegmentator to have an all-in-one comprehensive selection of segmentations. In addition, it integrates into clinical workflows as a DICOM node-triggered service using the open-source Orthanc research PACS (Picture Archiving and Communication System) server to make the automated segmentation algorithms available to clinicians. The BCA model's performance was evaluated using the Sørensen-Dice score. Finally, the segmentations from the 3 different tools (BCA, TotalSegmentator, and BOA) were compared by assessing the overall percentage of the segmented human body on a separate cohort of 150 whole-body CT scans. RESULTS The results showed that the BCA outperformed the previous publication, achieving a higher Sørensen-Dice score for the previously existing classes, including subcutaneous tissue (0.971 vs 0.962), muscle (0.959 vs 0.933), abdominal cavity (0.983 vs 0.973), thoracic cavity (0.982 vs 0.965), bone (0.961 vs 0.942), and an overall good segmentation efficiency for newly introduced classes: brain (0.985), breast implant (0.943), glands (0.766), mediastinum (0.880), pericardium (0.964), and spinal cord (0.896). All in all, it achieved a 0.935 average Sørensen-Dice score, which is comparable to the one of the TotalSegmentator (0.94). The TotalSegmentator had a mean voxel body coverage of 31% ± 6%, whereas BCA had a coverage of 75% ± 6% and BOA achieved 93% ± 2%. CONCLUSIONS The open-source BOA merges different segmentation algorithms with a focus on workflow integration through DICOM node integration, offering a comprehensive body segmentation in CT images with a high coverage of the body volume.
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Affiliation(s)
- Johannes Haubold
- From the Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany (J.H., G.B., V.P., B.M.S., S.K., L.K., N.v.L., L.U., M.F., F.N., R.H.); and Institute of Artificial Intelligence in Medicine, University Hospital Essen, Essen, Germany (J.H., G.B., V.P., S.K., L.U., M.F., F.N., R.H.)
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Westhölter D, Haubold J, Welsner M, Salhöfer L, Wienker J, Sutharsan S, Straßburg S, Taube C, Umutlu L, Schaarschmidt BM, Koitka S, Zensen S, Forsting M, Nensa F, Hosch R, Opitz M. Elexacaftor/tezacaftor/ivacaftor influences body composition in adults with cystic fibrosis: a fully automated CT-based analysis. Sci Rep 2024; 14:9465. [PMID: 38658613 PMCID: PMC11043331 DOI: 10.1038/s41598-024-59622-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2024] [Accepted: 04/11/2024] [Indexed: 04/26/2024] Open
Abstract
A poor nutritional status is associated with worse pulmonary function and survival in people with cystic fibrosis (pwCF). CF transmembrane conductance regulator modulators can improve pulmonary function and body weight, but more data is needed to evaluate its effects on body composition. In this retrospective study, a pre-trained deep-learning network was used to perform a fully automated body composition analysis on chest CTs from 66 adult pwCF before and after receiving elexacaftor/tezacaftor/ivacaftor (ETI) therapy. Muscle and adipose tissues were quantified and divided by bone volume to obtain body size-adjusted ratios. After receiving ETI therapy, marked increases were observed in all adipose tissue ratios among pwCF, including the total adipose tissue ratio (+ 46.21%, p < 0.001). In contrast, only small, but statistically significant increases of the muscle ratio were measured in the overall study population (+ 1.63%, p = 0.008). Study participants who were initially categorized as underweight experienced more pronounced effects on total adipose tissue ratio (p = 0.002), while gains in muscle ratio were equally distributed across BMI categories (p = 0.832). Our findings suggest that ETI therapy primarily affects adipose tissues, not muscle tissue, in adults with CF. These effects are primarily observed among pwCF who were initially underweight. Our findings may have implications for the future nutritional management of pwCF.
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Affiliation(s)
- Dirk Westhölter
- Department of Pulmonary Medicine, University Hospital Essen-Ruhrlandklinik, Essen, Germany
| | - Johannes Haubold
- Institute for Artificial Intelligence in Medicine, University Hospital Essen, Essen, Germany
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany
| | - Matthias Welsner
- Department of Pulmonary Medicine, University Hospital Essen-Ruhrlandklinik, Essen, Germany
- Adult Cystic Fibrosis Center, Department of Pulmonary Medicine, University Hospital Essen-Ruhrlandklinik, Essen, Germany
| | - Luca Salhöfer
- Institute for Artificial Intelligence in Medicine, University Hospital Essen, Essen, Germany
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany
| | - Johannes Wienker
- Department of Pulmonary Medicine, University Hospital Essen-Ruhrlandklinik, Essen, Germany
| | - Sivagurunathan Sutharsan
- Department of Pulmonary Medicine, University Hospital Essen-Ruhrlandklinik, Essen, Germany
- Adult Cystic Fibrosis Center, Department of Pulmonary Medicine, University Hospital Essen-Ruhrlandklinik, Essen, Germany
| | - Svenja Straßburg
- Department of Pulmonary Medicine, University Hospital Essen-Ruhrlandklinik, Essen, Germany
- Adult Cystic Fibrosis Center, Department of Pulmonary Medicine, University Hospital Essen-Ruhrlandklinik, Essen, Germany
| | - Christian Taube
- Department of Pulmonary Medicine, University Hospital Essen-Ruhrlandklinik, Essen, Germany
| | - Lale Umutlu
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany
| | - Benedikt M Schaarschmidt
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany
| | - Sven Koitka
- Institute for Artificial Intelligence in Medicine, University Hospital Essen, Essen, Germany
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany
| | - Sebastian Zensen
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany
| | - Michael Forsting
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany
| | - Felix Nensa
- Institute for Artificial Intelligence in Medicine, University Hospital Essen, Essen, Germany
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany
| | - René Hosch
- Institute for Artificial Intelligence in Medicine, University Hospital Essen, Essen, Germany
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany
| | - Marcel Opitz
- Institute for Artificial Intelligence in Medicine, University Hospital Essen, Essen, Germany.
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany.
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Wienker J, Darwiche K, Rüsche N, Büscher E, Karpf-Wissel R, Winantea J, Özkan F, Westhölter D, Taube C, Kersting D, Hautzel H, Salhöfer L, Hosch R, Nensa F, Forsting M, Schaarschmidt BM, Zensen S, Theysohn J, Umutlu L, Haubold J, Opitz M. Body composition impacts outcome of bronchoscopic lung volume reduction in patients with severe emphysema: a fully automated CT-based analysis. Sci Rep 2024; 14:8718. [PMID: 38622275 PMCID: PMC11018765 DOI: 10.1038/s41598-024-58628-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2023] [Accepted: 04/01/2024] [Indexed: 04/17/2024] Open
Abstract
Chronic Obstructive Pulmonary Disease (COPD) is characterized by progressive and irreversible airflow limitation, with individual body composition influencing disease severity. Severe emphysema worsens symptoms through hyperinflation, which can be relieved by bronchoscopic lung volume reduction (BLVR). To investigate how body composition, assessed through CT scans, impacts outcomes in emphysema patients undergoing BLVR. Fully automated CT-based body composition analysis (BCA) was performed in patients with end-stage emphysema receiving BLVR with valves. Post-interventional muscle and adipose tissues were quantified, body size-adjusted, and compared to baseline parameters. Between January 2015 and December 2022, 300 patients with severe emphysema underwent endobronchial valve treatment. Significant improvements were seen in outcome parameters, which were defined as changes in pulmonary function, physical performance, and quality of life (QoL) post-treatment. Muscle volume remained stable (1.632 vs. 1.635 for muscle bone adjusted ratio (BAR) at baseline and after 6 months respectively), while bone adjusted adipose tissue volumes, especially total and pericardial adipose tissue, showed significant increase (2.86 vs. 3.00 and 0.16 vs. 0.17, respectively). Moderate to strong correlations between bone adjusted muscle volume and weaker correlations between adipose tissue volumes and outcome parameters (pulmonary function, QoL and physical performance) were observed. Particularly after 6-month, bone adjusted muscle volume changes positively corresponded to improved outcomes (ΔForced expiratory volume in 1 s [FEV1], r = 0.440; ΔInspiratory vital capacity [IVC], r = 0.397; Δ6Minute walking distance [6MWD], r = 0.509 and ΔCOPD assessment test [CAT], r = -0.324; all p < 0.001). Group stratification by bone adjusted muscle volume changes revealed that groups with substantial muscle gain experienced a greater clinical benefit in pulmonary function improvements, QoL and physical performance (ΔFEV1%, 5.5 vs. 39.5; ΔIVC%, 4.3 vs. 28.4; Δ6MWDm, 14 vs. 110; ΔCATpts, -2 vs. -3.5 for groups with ΔMuscle, BAR% < -10 vs. > 10, respectively). BCA results among patients divided by the minimal clinically important difference for forced expiratory volume of the first second (FEV1) showed significant differences in bone-adjusted muscle and intramuscular adipose tissue (IMAT) volumes and their respective changes after 6 months (ΔMuscle, BAR% -5 vs. 3.4 and ΔIMAT, BAR% -0.62 vs. 0.60 for groups with ΔFEV1 ≤ 100 mL vs > 100 mL). Altered body composition, especially increased muscle volume, is associated with functional improvements in BLVR-treated patients.
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Affiliation(s)
- Johannes Wienker
- Division of Interventional Pneumology, Department of Pulmonary Medicine, University Medicine Essen-Ruhrlandklinik, Tüschener Weg 40, 45239, Essen, Germany.
| | - Kaid Darwiche
- Division of Interventional Pneumology, Department of Pulmonary Medicine, University Medicine Essen-Ruhrlandklinik, Tüschener Weg 40, 45239, Essen, Germany
| | - Nele Rüsche
- Division of Interventional Pneumology, Department of Pulmonary Medicine, University Medicine Essen-Ruhrlandklinik, Tüschener Weg 40, 45239, Essen, Germany
| | - Erik Büscher
- Division of Interventional Pneumology, Department of Pulmonary Medicine, University Medicine Essen-Ruhrlandklinik, Tüschener Weg 40, 45239, Essen, Germany
| | - Rüdiger Karpf-Wissel
- Division of Interventional Pneumology, Department of Pulmonary Medicine, University Medicine Essen-Ruhrlandklinik, Tüschener Weg 40, 45239, Essen, Germany
| | - Jane Winantea
- Division of Interventional Pneumology, Department of Pulmonary Medicine, University Medicine Essen-Ruhrlandklinik, Tüschener Weg 40, 45239, Essen, Germany
| | - Filiz Özkan
- Division of Interventional Pneumology, Department of Pulmonary Medicine, University Medicine Essen-Ruhrlandklinik, Tüschener Weg 40, 45239, Essen, Germany
| | - Dirk Westhölter
- Department of Pulmonary Medicine, University Medicine Essen-Ruhrlandklinik, Essen, Germany
| | - Christian Taube
- Department of Pulmonary Medicine, University Medicine Essen-Ruhrlandklinik, Essen, Germany
| | - David Kersting
- Department of Nuclear Medicine, University Hospital Essen, Essen, Germany
| | - Hubertus Hautzel
- Department of Nuclear Medicine, University Hospital Essen, Essen, Germany
| | - Luca Salhöfer
- Institute for Artificial Intelligence in Medicine, University Hospital Essen, Essen, Germany
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany
| | - René Hosch
- Institute for Artificial Intelligence in Medicine, University Hospital Essen, Essen, Germany
| | - Felix Nensa
- Institute for Artificial Intelligence in Medicine, University Hospital Essen, Essen, Germany
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany
| | - Michael Forsting
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany
| | - Benedikt M Schaarschmidt
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany
| | - Sebastian Zensen
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany
| | - Jens Theysohn
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany
| | - Lale Umutlu
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany
| | - Johannes Haubold
- Institute for Artificial Intelligence in Medicine, University Hospital Essen, Essen, Germany
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany
| | - Marcel Opitz
- Institute for Artificial Intelligence in Medicine, University Hospital Essen, Essen, Germany
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany
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Bender RG, Sirota SB, Swetschinski LR, Dominguez RMV, Novotney A, Wool EE, Ikuta KS, Vongpradith A, Rogowski ELB, Doxey M, Troeger CE, Albertson SB, Ma J, He J, Maass KL, A.F.Simões E, Abdoun M, Abdul Aziz JM, Abdulah DM, Abu Rumeileh S, Abualruz H, Aburuz S, Adepoju AV, Adha R, Adikusuma W, Adra S, Afraz A, Aghamiri S, Agodi A, Ahmadzade AM, Ahmed H, Ahmed A, Akinosoglou K, AL-Ahdal TMA, Al-amer RM, Albashtawy M, AlBataineh MT, Alemi H, Al-Gheethi AAS, Ali A, Ali SSS, Alqahtani JS, AlQudah M, Al-Tawfiq JA, Al-Worafi YM, Alzoubi KH, Amani R, Amegbor PM, Ameyaw EK, Amuasi JH, Anil A, Anyanwu PE, Arafat M, Areda D, Arefnezhad R, Atalell KA, Ayele F, Azzam AY, Babamohamadi H, Babin FX, Bahurupi Y, Baker S, Banik B, Barchitta M, Barqawi HJ, Basharat Z, Baskaran P, Batra K, Batra R, Bayileyegn NS, Beloukas A, Berkley JA, Beyene KA, Bhargava A, Bhattacharjee P, Bielicki JA, Bilalaga MM, Bitra VR, Brown CS, Burkart K, Bustanji Y, Carr S, Chahine Y, Chattu VK, Chichagi F, Chopra H, Chukwu IS, Chung E, Dadana S, Dai X, Dandona L, Dandona R, Darban I, Dash NR, Dashti M, Dashtkoohi M, Dekker DM, Delgado-Enciso I, Devanbu VGC, Dhama K, Diao N, Do THP, Dokova KG, Dolecek C, Dziedzic AM, Eckmanns T, Ed-Dra A, Efendi F, Eftekharimehrabad A, Eyre DW, Fahim A, Feizkhah A, Felton TW, Ferreira N, Flor LS, Gaihre S, Gebregergis MW, Gebrehiwot M, Geffers C, Gerema U, Ghaffari K, Goldust M, Goleij P, Guan SY, Gudeta MD, Guo C, Gupta VB, Gupta I, Habibzadeh F, Hadi NR, Haeuser E, Hailu WB, Hajibeygi R, Haj-Mirzaian A, Haller S, Hamiduzzaman M, Hanifi N, Hansel J, Hasnain MS, Haubold J, Hoan NQ, Huynh HH, Iregbu KC, Islam MR, Jafarzadeh A, Jairoun AA, Jalili M, Jomehzadeh N, Joshua CE, Kabir MA, Kamal Z, Kanmodi KK, Kantar RS, Karimi Behnagh A, Kaur N, Kaur H, Khamesipour F, Khan MN, Khan suheb MZ, Khanal V, Khatab K, Khatib MN, Kim G, Kim K, Kitila ATT, Komaki S, Krishan K, Krumkamp R, Kuddus MA, Kurniasari MD, Lahariya C, Latifinaibin K, Le NHH, Le TTT, Le TDT, Lee SW, LEPAPE A, Lerango TL, Li MC, Mahboobipour AA, Malhotra K, Mallhi TH, Manoharan A, Martinez-Guerra BA, Mathioudakis AG, Mattiello R, May J, McManigal B, McPhail SM, Mekene Meto T, Mendez-Lopez MAM, Meo SA, Merati M, Mestrovic T, Mhlanga L, Minh LHN, Misganaw A, Mishra V, Misra AK, Mohamed NS, Mohammadi E, Mohammed M, Mohammed M, Mokdad AH, Monasta L, Moore CE, Motappa R, Mougin V, Mousavi P, Mulita F, Mulu AA, Naghavi P, Naik GR, Nainu F, Nair TS, Nargus S, Negaresh M, Nguyen HTH, Nguyen DH, Nguyen VT, Nikolouzakis TK, Noman EA, Nri-Ezedi CA, Odetokun IA, Okwute PG, Olana MD, Olanipekun TO, Olasupo OO, Olivas-Martinez A, Ordak M, Ortiz-Brizuela E, Ouyahia A, Padubidri JR, Pak A, Pandey A, Pantazopoulos I, Parija PP, Parikh RR, Park S, Parthasarathi A, Pashaei A, Peprah P, Pham HT, Poddighe D, Pollard A, Ponce-De-Leon A, Prakash PY, Prates EJS, Quan NK, Raee P, Rahim F, Rahman M, Rahmati M, Ramasamy SK, Ranjan S, Rao IR, Rashid AM, Rattanavong S, Ravikumar N, Reddy MMRK, Redwan EMM, Reiner RC, Reyes LF, Roberts T, Rodrigues M, Rosenthal VD, Roy P, Runghien T, Saeed U, Saghazadeh A, Saheb Sharif-Askari N, Saheb Sharif-Askari F, Sahoo SS, Sahu M, Sakshaug JW, Salami AA, Saleh MA, Salehi omran H, Sallam M, Samadzadeh S, Samodra YL, Sanjeev RK, Sarasmita MA, Saravanan A, Sartorius B, Saulam J, Schumacher AE, Seyedi SA, Shafie M, Shahid S, Sham S, Shamim MA, Shamshirgaran MA, Shastry RP, Sherchan SP, Shiferaw D, Shittu A, Siddig EE, Sinto R, Sood A, Sorensen RJD, Stergachis A, Stoeva TZ, Swain CK, Szarpak L, Tamuzi JL, Temsah MH, Tessema MBT, Thangaraju P, Tran NM, Tran NH, Tumurkhuu M, Ty SS, Udoakang AJ, Ulhaq I, Umar TP, Umer AA, Vahabi SM, Vaithinathan AG, Van den Eynde J, Walson JL, Waqas M, Xing Y, Yadav MK, Yahya G, Yon DK, Zahedi Bialvaei A, Zakham F, Zeleke AM, Zhai C, Zhang Z, Zhang H, Zielińska M, Zheng P, Aravkin AY, Vos T, Hay SI, Mosser JF, Lim SS, Naghavi M, Murray CJL, Kyu HH. Global, regional, and national incidence and mortality burden of non-COVID-19 lower respiratory infections and aetiologies, 1990-2021: a systematic analysis from the Global Burden of Disease Study 2021. Lancet Infect Dis 2024:S1473-3099(24)00176-2. [PMID: 38636536 DOI: 10.1016/s1473-3099(24)00176-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/16/2023] [Revised: 02/19/2024] [Accepted: 03/07/2024] [Indexed: 04/20/2024]
Abstract
BACKGROUND Lower respiratory infections (LRIs) are a major global contributor to morbidity and mortality. In 2020-21, non-pharmaceutical interventions associated with the COVID-19 pandemic reduced not only the transmission of SARS-CoV-2, but also the transmission of other LRI pathogens. Tracking LRI incidence and mortality, as well as the pathogens responsible, can guide health-system responses and funding priorities to reduce future burden. We present estimates from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 of the burden of non-COVID-19 LRIs and corresponding aetiologies from 1990 to 2021, inclusive of pandemic effects on the incidence and mortality of select respiratory viruses, globally, regionally, and for 204 countries and territories. METHODS We estimated mortality, incidence, and aetiology attribution for LRI, defined by the GBD as pneumonia or bronchiolitis, not inclusive of COVID-19. We analysed 26 259 site-years of mortality data using the Cause of Death Ensemble model to estimate LRI mortality rates. We analysed all available age-specific and sex-specific data sources, including published literature identified by a systematic review, as well as household surveys, hospital admissions, health insurance claims, and LRI mortality estimates, to generate internally consistent estimates of incidence and prevalence using DisMod-MR 2.1. For aetiology estimation, we analysed multiple causes of death, vital registration, hospital discharge, microbial laboratory, and literature data using a network analysis model to produce the proportion of LRI deaths and episodes attributable to the following pathogens: Acinetobacter baumannii, Chlamydia spp, Enterobacter spp, Escherichia coli, fungi, group B streptococcus, Haemophilus influenzae, influenza viruses, Klebsiella pneumoniae, Legionella spp, Mycoplasma spp, polymicrobial infections, Pseudomonas aeruginosa, respiratory syncytial virus (RSV), Staphylococcus aureus, Streptococcus pneumoniae, and other viruses (ie, the aggregate of all viruses studied except influenza and RSV), as well as a residual category of other bacterial pathogens. FINDINGS Globally, in 2021, we estimated 344 million (95% uncertainty interval [UI] 325-364) incident episodes of LRI, or 4350 episodes (4120-4610) per 100 000 population, and 2·18 million deaths (1·98-2·36), or 27·7 deaths (25·1-29·9) per 100 000. 502 000 deaths (406 000-611 000) were in children younger than 5 years, among which 254 000 deaths (197 000-320 000) occurred in countries with a low Socio-demographic Index. Of the 18 modelled pathogen categories in 2021, S pneumoniae was responsible for the highest proportions of LRI episodes and deaths, with an estimated 97·9 million (92·1-104·0) episodes and 505 000 deaths (454 000-555 000) globally. The pathogens responsible for the second and third highest episode counts globally were other viral aetiologies (46·4 million [43·6-49·3] episodes) and Mycoplasma spp (25·3 million [23·5-27·2]), while those responsible for the second and third highest death counts were S aureus (424 000 [380 000-459 000]) and K pneumoniae (176 000 [158 000-194 000]). From 1990 to 2019, the global all-age non-COVID-19 LRI mortality rate declined by 41·7% (35·9-46·9), from 56·5 deaths (51·3-61·9) to 32·9 deaths (29·9-35·4) per 100 000. From 2019 to 2021, during the COVID-19 pandemic and implementation of associated non-pharmaceutical interventions, we estimated a 16·0% (13·1-18·6) decline in the global all-age non-COVID-19 LRI mortality rate, largely accounted for by a 71·8% (63·8-78·9) decline in the number of influenza deaths and a 66·7% (56·6-75·3) decline in the number of RSV deaths. INTERPRETATION Substantial progress has been made in reducing LRI mortality, but the burden remains high, especially in low-income and middle-income countries. During the COVID-19 pandemic, with its associated non-pharmaceutical interventions, global incident LRI cases and mortality attributable to influenza and RSV declined substantially. Expanding access to health-care services and vaccines, including S pneumoniae, H influenzae type B, and novel RSV vaccines, along with new low-cost interventions against S aureus, could mitigate the LRI burden and prevent transmission of LRI-causing pathogens. FUNDING Bill & Melinda Gates Foundation, Wellcome Trust, and Department of Health and Social Care (UK).
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Ferrari AJ, Santomauro DF, Aali A, Abate YH, Abbafati C, Abbastabar H, Abd ElHafeez S, Abdelmasseh M, Abd-Elsalam S, Abdollahi A, Abdullahi A, Abegaz KH, Abeldaño Zuñiga RA, Aboagye RG, Abolhassani H, Abreu LG, Abualruz H, Abu-Gharbieh E, Abu-Rmeileh NME, Ackerman IN, Addo IY, Addolorato G, Adebiyi AO, Adepoju AV, Adewuyi HO, Afyouni S, Afzal S, Afzal S, Agodi A, Ahmad A, Ahmad D, Ahmad F, Ahmad S, Ahmed A, Ahmed LA, Ahmed MB, Ajami M, Akinosoglou K, Akkaif MA, Al Hasan SM, Alalalmeh SO, Al-Aly Z, Albashtawy M, Aldridge RW, Alemu MD, Alemu YM, Alene KA, Al-Gheethi AAS, Alharrasi M, Alhassan RK, Ali MU, Ali R, Ali SSS, Alif SM, Aljunid SM, Al-Marwani S, Almazan JU, Alomari MA, Al-Omari B, Altaany Z, Alvis-Guzman N, Alvis-Zakzuk NJ, Alwafi H, Al-Wardat MS, Al-Worafi YM, Aly S, Alzoubi KH, Amare AT, Amegbor PM, Ameyaw EK, Amin TT, Amindarolzarbi A, Amiri S, Amugsi DA, Ancuceanu R, Anderlini D, Anderson DB, Andrade PP, Andrei CL, Ansari H, Antony CM, Anwar S, Anwar SL, Anwer R, Anyanwu PE, Arab JP, Arabloo J, Arafat M, Araki DT, Aravkin AY, Arkew M, Armocida B, Arndt MB, Arooj M, Artamonov AA, Aruleba RT, Arumugam A, Ashbaugh C, Ashemo MY, Ashraf M, Asika MO, Askari E, Astell-Burt T, Athari SS, Atorkey P, Atout MMW, Atreya A, Aujayeb A, Ausloos M, Avan A, Awotidebe AW, Awuviry-Newton K, Ayala Quintanilla BP, Ayuso-Mateos JL, Azadnajafabad S, Azevedo RMS, Babu AS, Badar M, Badiye AD, Baghdadi S, Bagheri N, Bah S, Bai R, Baker JL, Bakkannavar SM, Bako AT, Balakrishnan S, Bam K, Banik PC, Barchitta M, Bardhan M, Bardideh E, Barker-Collo SL, Barqawi HJ, Barrow A, Barteit S, Barua L, Bashiri Aliabadi S, Basiru A, Basu S, Basu S, Bathini PP, Batra K, Baune BT, Bayileyegn NS, Behnam B, Behnoush AH, Beiranvand M, Bejarano Ramirez DF, Bell ML, Bello OO, Beloukas A, Bensenor IM, Berezvai Z, Bernabe E, Bernstein RS, Bettencourt PJG, Bhagavathula AS, Bhala N, Bhandari D, Bhargava A, Bhaskar S, Bhat V, Bhatti GK, Bhatti JS, Bhatti MS, Bhatti R, Bhutta ZA, Bikbov B, Bishai JD, 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Global incidence, prevalence, years lived with disability (YLDs), disability-adjusted life-years (DALYs), and healthy life expectancy (HALE) for 371 diseases and injuries in 204 countries and territories and 811 subnational locations, 1990-2021: a systematic analysis for the Global Burden of Disease Study 2021. Lancet 2024:S0140-6736(24)00757-8. [PMID: 38642570 DOI: 10.1016/s0140-6736(24)00757-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/22/2023] [Revised: 03/07/2024] [Accepted: 04/12/2024] [Indexed: 04/22/2024]
Abstract
BACKGROUND Detailed, comprehensive, and timely reporting on population health by underlying causes of disability and premature death is crucial to understanding and responding to complex patterns of disease and injury burden over time and across age groups, sexes, and locations. The availability of disease burden estimates can promote evidence-based interventions that enable public health researchers, policy makers, and other professionals to implement strategies that can mitigate diseases. It can also facilitate more rigorous monitoring of progress towards national and international health targets, such as the Sustainable Development Goals. For three decades, the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) has filled that need. A global network of collaborators contributed to the production of GBD 2021 by providing, reviewing, and analysing all available data. GBD estimates are updated routinely with additional data and refined analytical methods. GBD 2021 presents, for the first time, estimates of health loss due to the COVID-19 pandemic. METHODS The GBD 2021 disease and injury burden analysis estimated years lived with disability (YLDs), years of life lost (YLLs), disability-adjusted life-years (DALYs), and healthy life expectancy (HALE) for 371 diseases and injuries using 100 983 data sources. Data were extracted from vital registration systems, verbal autopsies, censuses, household surveys, disease-specific registries, health service contact data, and other sources. YLDs were calculated by multiplying cause-age-sex-location-year-specific prevalence of sequelae by their respective disability weights, for each disease and injury. YLLs were calculated by multiplying cause-age-sex-location-year-specific deaths by the standard life expectancy at the age that death occurred. DALYs were calculated by summing YLDs and YLLs. HALE estimates were produced using YLDs per capita and age-specific mortality rates by location, age, sex, year, and cause. 95% uncertainty intervals (UIs) were generated for all final estimates as the 2·5th and 97·5th percentiles values of 500 draws. Uncertainty was propagated at each step of the estimation process. Counts and age-standardised rates were calculated globally, for seven super-regions, 21 regions, 204 countries and territories (including 21 countries with subnational locations), and 811 subnational locations, from 1990 to 2021. Here we report data for 2010 to 2021 to highlight trends in disease burden over the past decade and through the first 2 years of the COVID-19 pandemic. FINDINGS Global DALYs increased from 2·63 billion (95% UI 2·44-2·85) in 2010 to 2·88 billion (2·64-3·15) in 2021 for all causes combined. Much of this increase in the number of DALYs was due to population growth and ageing, as indicated by a decrease in global age-standardised all-cause DALY rates of 14·2% (95% UI 10·7-17·3) between 2010 and 2019. Notably, however, this decrease in rates reversed during the first 2 years of the COVID-19 pandemic, with increases in global age-standardised all-cause DALY rates since 2019 of 4·1% (1·8-6·3) in 2020 and 7·2% (4·7-10·0) in 2021. In 2021, COVID-19 was the leading cause of DALYs globally (212·0 million [198·0-234·5] DALYs), followed by ischaemic heart disease (188·3 million [176·7-198·3]), neonatal disorders (186·3 million [162·3-214·9]), and stroke (160·4 million [148·0-171·7]). However, notable health gains were seen among other leading communicable, maternal, neonatal, and nutritional (CMNN) diseases. Globally between 2010 and 2021, the age-standardised DALY rates for HIV/AIDS decreased by 47·8% (43·3-51·7) and for diarrhoeal diseases decreased by 47·0% (39·9-52·9). Non-communicable diseases contributed 1·73 billion (95% UI 1·54-1·94) DALYs in 2021, with a decrease in age-standardised DALY rates since 2010 of 6·4% (95% UI 3·5-9·5). Between 2010 and 2021, among the 25 leading Level 3 causes, age-standardised DALY rates increased most substantially for anxiety disorders (16·7% [14·0-19·8]), depressive disorders (16·4% [11·9-21·3]), and diabetes (14·0% [10·0-17·4]). Age-standardised DALY rates due to injuries decreased globally by 24·0% (20·7-27·2) between 2010 and 2021, although improvements were not uniform across locations, ages, and sexes. Globally, HALE at birth improved slightly, from 61·3 years (58·6-63·6) in 2010 to 62·2 years (59·4-64·7) in 2021. However, despite this overall increase, HALE decreased by 2·2% (1·6-2·9) between 2019 and 2021. INTERPRETATION Putting the COVID-19 pandemic in the context of a mutually exclusive and collectively exhaustive list of causes of health loss is crucial to understanding its impact and ensuring that health funding and policy address needs at both local and global levels through cost-effective and evidence-based interventions. A global epidemiological transition remains underway. Our findings suggest that prioritising non-communicable disease prevention and treatment policies, as well as strengthening health systems, continues to be crucially important. The progress on reducing the burden of CMNN diseases must not stall; although global trends are improving, the burden of CMNN diseases remains unacceptably high. Evidence-based interventions will help save the lives of young children and mothers and improve the overall health and economic conditions of societies across the world. Governments and multilateral organisations should prioritise pandemic preparedness planning alongside efforts to reduce the burden of diseases and injuries that will strain resources in the coming decades. FUNDING Bill & Melinda Gates Foundation.
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Naghavi M, Ong KL, Aali A, Ababneh HS, Abate YH, Abbafati C, Abbasgholizadeh R, Abbasian M, Abbasi-Kangevari M, Abbastabar H, Abd ElHafeez S, Abdelmasseh M, Abd-Elsalam S, Abdelwahab A, Abdollahi M, Abdollahifar MA, Abdoun M, Abdulah DM, Abdullahi A, Abebe M, Abebe SS, Abedi A, Abegaz KH, Abhilash ES, Abidi H, Abiodun O, Aboagye RG, Abolhassani H, Abolmaali M, Abouzid M, Aboye GB, Abreu LG, Abrha WA, Abtahi D, Abu Rumeileh S, Abualruz H, Abubakar B, Abu-Gharbieh E, Abu-Rmeileh NME, Aburuz S, Abu-Zaid A, Accrombessi MMK, Adal TG, Adamu AA, Addo IY, Addolorato G, Adebiyi AO, Adekanmbi V, Adepoju AV, Adetunji CO, Adetunji JB, Adeyeoluwa TE, Adeyinka DA, Adeyomoye OI, Admass BAA, Adnani QES, Adra S, Afolabi AA, Afzal MS, Afzal S, Agampodi SB, Agasthi P, Aggarwal M, Aghamiri S, Agide FD, Agodi A, Agrawal A, Agyemang-Duah W, Ahinkorah BO, Ahmad A, Ahmad D, Ahmad F, Ahmad MM, Ahmad S, Ahmad S, Ahmad T, Ahmadi K, Ahmadzade AM, Ahmed A, Ahmed A, Ahmed H, Ahmed LA, Ahmed MS, Ahmed MS, Ahmed MB, 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Jadidi-Niaragh F, Jafarinia M, Jafarzadeh A, Jaggi K, Jahankhani K, Jahanmehr N, Jahrami H, Jain N, Jairoun AA, Jaiswal A, Jamshidi E, Janko MM, Jatau AI, Javadov S, Javaheri T, Jayapal SK, Jayaram S, Jebai R, Jee SH, Jeganathan J, Jha AK, Jha RP, Jiang H, Jin Y, Johnson O, Jokar M, Jonas JB, Joo T, Joseph A, Joseph N, Joshua CE, Joshy G, Jozwiak JJ, Jürisson M, K V, Kaambwa B, Kabir A, Kabir Z, Kadashetti V, Kadir DH, Kalani R, Kalankesh LR, Kalankesh LR, Kaliyadan F, Kalra S, Kamal VK, Kamarajah SK, Kamath R, Kamiab Z, Kamyari N, Kanagasabai T, Kanchan T, Kandel H, Kanmanthareddy AR, Kanmiki EW, Kanmodi KK, Kannan S S, Kansal SK, Kantar RS, Kapoor N, Karajizadeh M, Karanth SD, Karasneh RA, Karaye IM, Karch A, Karim A, Karimi SE, Karimi Behnagh A, Kashoo FZ, Kasnazani QHA, Kasraei H, Kassebaum NJ, Kassel MB, Kauppila JH, Kaur N, Kawakami N, Kayode GA, Kazemi F, Kazemian S, Kazmi TH, Kebebew GM, Kebede AD, Kebede F, Keflie TS, Keiyoro PN, Keller C, Kelly JT, Kempen JH, Kerr JA, 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Global burden of 288 causes of death and life expectancy decomposition in 204 countries and territories and 811 subnational locations, 1990-2021: a systematic analysis for the Global Burden of Disease Study 2021. Lancet 2024:S0140-6736(24)00367-2. [PMID: 38582094 DOI: 10.1016/s0140-6736(24)00367-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/13/2023] [Revised: 02/15/2024] [Accepted: 02/22/2024] [Indexed: 04/08/2024]
Abstract
BACKGROUND Regular, detailed reporting on population health by underlying cause of death is fundamental for public health decision making. Cause-specific estimates of mortality and the subsequent effects on life expectancy worldwide are valuable metrics to gauge progress in reducing mortality rates. These estimates are particularly important following large-scale mortality spikes, such as the COVID-19 pandemic. When systematically analysed, mortality rates and life expectancy allow comparisons of the consequences of causes of death globally and over time, providing a nuanced understanding of the effect of these causes on global populations. METHODS The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 cause-of-death analysis estimated mortality and years of life lost (YLLs) from 288 causes of death by age-sex-location-year in 204 countries and territories and 811 subnational locations for each year from 1990 until 2021. The analysis used 56 604 data sources, including data from vital registration and verbal autopsy as well as surveys, censuses, surveillance systems, and cancer registries, among others. As with previous GBD rounds, cause-specific death rates for most causes were estimated using the Cause of Death Ensemble model-a modelling tool developed for GBD to assess the out-of-sample predictive validity of different statistical models and covariate permutations and combine those results to produce cause-specific mortality estimates-with alternative strategies adapted to model causes with insufficient data, substantial changes in reporting over the study period, or unusual epidemiology. YLLs were computed as the product of the number of deaths for each cause-age-sex-location-year and the standard life expectancy at each age. As part of the modelling process, uncertainty intervals (UIs) were generated using the 2·5th and 97·5th percentiles from a 1000-draw distribution for each metric. We decomposed life expectancy by cause of death, location, and year to show cause-specific effects on life expectancy from 1990 to 2021. We also used the coefficient of variation and the fraction of population affected by 90% of deaths to highlight concentrations of mortality. Findings are reported in counts and age-standardised rates. Methodological improvements for cause-of-death estimates in GBD 2021 include the expansion of under-5-years age group to include four new age groups, enhanced methods to account for stochastic variation of sparse data, and the inclusion of COVID-19 and other pandemic-related mortality-which includes excess mortality associated with the pandemic, excluding COVID-19, lower respiratory infections, measles, malaria, and pertussis. For this analysis, 199 new country-years of vital registration cause-of-death data, 5 country-years of surveillance data, 21 country-years of verbal autopsy data, and 94 country-years of other data types were added to those used in previous GBD rounds. FINDINGS The leading causes of age-standardised deaths globally were the same in 2019 as they were in 1990; in descending order, these were, ischaemic heart disease, stroke, chronic obstructive pulmonary disease, and lower respiratory infections. In 2021, however, COVID-19 replaced stroke as the second-leading age-standardised cause of death, with 94·0 deaths (95% UI 89·2-100·0) per 100 000 population. The COVID-19 pandemic shifted the rankings of the leading five causes, lowering stroke to the third-leading and chronic obstructive pulmonary disease to the fourth-leading position. In 2021, the highest age-standardised death rates from COVID-19 occurred in sub-Saharan Africa (271·0 deaths [250·1-290·7] per 100 000 population) and Latin America and the Caribbean (195·4 deaths [182·1-211·4] per 100 000 population). The lowest age-standardised death rates from COVID-19 were in the high-income super-region (48·1 deaths [47·4-48·8] per 100 000 population) and southeast Asia, east Asia, and Oceania (23·2 deaths [16·3-37·2] per 100 000 population). Globally, life expectancy steadily improved between 1990 and 2019 for 18 of the 22 investigated causes. Decomposition of global and regional life expectancy showed the positive effect that reductions in deaths from enteric infections, lower respiratory infections, stroke, and neonatal deaths, among others have contributed to improved survival over the study period. However, a net reduction of 1·6 years occurred in global life expectancy between 2019 and 2021, primarily due to increased death rates from COVID-19 and other pandemic-related mortality. Life expectancy was highly variable between super-regions over the study period, with southeast Asia, east Asia, and Oceania gaining 8·3 years (6·7-9·9) overall, while having the smallest reduction in life expectancy due to COVID-19 (0·4 years). The largest reduction in life expectancy due to COVID-19 occurred in Latin America and the Caribbean (3·6 years). Additionally, 53 of the 288 causes of death were highly concentrated in locations with less than 50% of the global population as of 2021, and these causes of death became progressively more concentrated since 1990, when only 44 causes showed this pattern. The concentration phenomenon is discussed heuristically with respect to enteric and lower respiratory infections, malaria, HIV/AIDS, neonatal disorders, tuberculosis, and measles. INTERPRETATION Long-standing gains in life expectancy and reductions in many of the leading causes of death have been disrupted by the COVID-19 pandemic, the adverse effects of which were spread unevenly among populations. Despite the pandemic, there has been continued progress in combatting several notable causes of death, leading to improved global life expectancy over the study period. Each of the seven GBD super-regions showed an overall improvement from 1990 and 2021, obscuring the negative effect in the years of the pandemic. Additionally, our findings regarding regional variation in causes of death driving increases in life expectancy hold clear policy utility. Analyses of shifting mortality trends reveal that several causes, once widespread globally, are now increasingly concentrated geographically. These changes in mortality concentration, alongside further investigation of changing risks, interventions, and relevant policy, present an important opportunity to deepen our understanding of mortality-reduction strategies. Examining patterns in mortality concentration might reveal areas where successful public health interventions have been implemented. Translating these successes to locations where certain causes of death remain entrenched can inform policies that work to improve life expectancy for people everywhere. FUNDING Bill & Melinda Gates Foundation.
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CCD, Iyer M, Jaan A, Jacob L, Jadidi-Niaragh F, Jafari M, Jafarinia M, Jafarzadeh A, Jahankhani K, Jahanmehr N, Jahrami H, Jaiswal A, Jakovljevic M, Jamora RDG, Jana S, Javadi N, Javed S, Javeed S, Jayapal SK, Jayaram S, Jiang H, Johnson CO, Johnson WD, Jokar M, Jonas JB, Joseph A, Joseph N, Joshua CE, Jürisson M, Kabir A, Kabir Z, Kabito GG, Kadashetti V, Kafi F, Kalani R, Kalantar F, Kaliyadan F, Kamath A, Kamath S, Kanchan T, Kandel A, Kandel H, Kanmodi KK, Karajizadeh M, Karami J, Karanth SD, Karaye IM, Karch A, Karimi A, Karimi H, Karimi Behnagh A, Kasraei H, Kassebaum NJ, Kauppila JH, Kaur H, Kaur N, Kayode GA, Kazemi F, Keikavoosi-Arani L, Keller C, Keykhaei M, Khadembashiri MA, Khader YS, Khafaie MA, Khajuria H, Khalaji A, Khamesipour F, Khammarnia M, Khan M, Khan MAB, Khan YH, Khan Suheb MZ, Khanmohammadi S, Khanna T, Khatab K, Khatatbeh H, Khatatbeh MM, Khateri S, Khatib MN, Khayat Kashani HR, Khonji MS, khorashadizadeh F, Khormali M, Khubchandani J, Kian S, Kim G, Kim J, Kim MS, Kim YJ, Kimokoti RW, Kisa A, Kisa S, Kivimäki M, Kochhar S, Kolahi AA, Koly KN, Kompani F, Koroshetz WJ, Kosen S, Kourosh Arami M, Koyanagi A, Kravchenko MA, Krishan K, Krishnamoorthy V, Kuate Defo B, Kuddus MA, Kumar A, Kumar GA, Kumar M, Kumar N, Kumsa NB, Kundu S, Kurniasari MD, Kusuma D, Kuttikkattu A, Kyu HH, La Vecchia C, Ladan MA, Lahariya C, Laksono T, Lal DK, Lallukka T, Lám J, Lami FH, Landires I, Langguth B, Lasrado S, Latief K, Latifinaibin K, Lau KMM, Laurens MB, Lawal BK, Le LKD, Le TTT, Ledda C, Lee M, Lee SW, Lee SW, Lee WC, Lee YH, Leonardi M, Lerango TL, Li MC, Li W, Ligade VS, Lim SS, Linehan C, Liu C, Liu J, Liu W, Lo CH, Lo WD, Lobo SW, Logroscino G, Lopes G, Lopukhov PD, Lorenzovici L, Lorkowski S, Loureiro JA, Lubinda J, Lucchetti G, Lutzky Saute R, Ma ZF, Mabrok M, Machoy M, Madadizadeh F, Magdy Abd El Razek M, Maghazachi AA, Maghbouli N, Mahjoub S, Mahmoudi M, Majeed A, Malagón-Rojas JN, Malakan Rad E, Malhotra K, Malik AA, Malik I, Mallhi TH, Malta DC, Manilal A, Mansouri V, Mansournia MA, Marasini BP, Marateb HR, Maroufi SF, Martinez-Raga J, Martini S, Martins-Melo FR, Martorell M, März W, Marzo RR, Massano J, Mathangasinghe Y, Mathews E, Maude RJ, Maugeri A, Maulik PK, Mayeli M, Mazaheri M, McAlinden C, McGrath JJ, Meena JK, Mehndiratta MM, Mendez-Lopez MAM, Mendoza W, Mendoza-Cano O, Menezes RG, Merati M, Meretoja A, Merkin A, Mersha AM, Mestrovic T, Mi T, Miazgowski T, Michalek IM, Mihretie ET, Minh LHN, Mirfakhraie R, Mirica A, Mirrakhimov EM, Mirzaei M, Misganaw A, Misra S, Mithra P, Mizana BA, Mohamadkhani A, Mohamed NS, Mohammadi E, Mohammadi H, Mohammadi S, Mohammadi S, Mohammadshahi M, Mohammed M, Mohammed S, Mohammed S, Mohan S, Mojiri-forushani H, Moka N, Mokdad AH, Molinaro S, Möller H, Monasta L, Moniruzzaman M, Montazeri F, Moradi M, Moradi Y, Moradi-Lakeh M, Moraga P, Morovatdar N, Morrison SD, Mosapour A, Mosser JF, Mossialos E, Motaghinejad M, Mousavi P, Mousavi SE, Mubarik S, Muccioli L, Mughal F, Mukoro GD, Mulita A, Mulita F, Musaigwa F, Mustafa A, Mustafa G, Muthu S, Nagarajan AJ, Naghavi P, Naik GR, Nainu F, Nair TS, Najmuldeen HHR, Nakhostin Ansari N, Nambi G, Namdar Areshtanab H, Nargus S, Nascimento BR, Naser AY, Nashwan AJJ, Nasoori H, Nasreldein A, Natto ZS, Nauman J, Nayak BP, Nazri-Panjaki A, Negaresh M, Negash H, Negoi I, Negoi RI, Negru SM, Nejadghaderi SA, Nematollahi MH, Nesbit OD, Newton CRJ, Nguyen DH, Nguyen HTH, Nguyen HQ, Nguyen NTT, Nguyen PT, Nguyen VT, Niazi RK, Nikolouzakis TK, Niranjan V, Nnyanzi LA, Noman EA, Noroozi N, Norrving B, Noubiap JJ, Nri-Ezedi CA, Ntaios G, Nuñez-Samudio V, Nurrika D, Oancea B, Odetokun IA, O'Donnell MJ, Ogunsakin RE, Oguta JO, Oh IH, Okati-Aliabad H, Okeke SR, Okekunle AP, Okonji OC, Okwute PG, Olagunju AT, Olaiya MT, Olana MD, Olatubi MI, Oliveira GMM, Olufadewa II, Olusanya BO, Omar Bali A, Ong S, Onwujekwe OE, Ordak M, Orji AU, Ortega-Altamirano DV, Osuagwu UL, Otstavnov N, Otstavnov SS, Ouyahia A, Owolabi MO, P A MP, Pacheco-Barrios K, Padubidri JR, Pal PK, Palange PN, Palladino C, Palladino R, Palma-Alvarez RF, Pan F, Panagiotakos D, Panda-Jonas S, Pandey A, Pandey A, Pandian JD, Pangaribuan HU, Pantazopoulos I, Pardhan S, Parija PP, Parikh RR, Park S, Parthasarathi A, Pashaei A, Patel J, Patil S, Patoulias D, Pawar S, Pedersini P, Pensato U, Pereira DM, Pereira J, Pereira MO, Peres MFP, Perico N, Perna S, Petcu IR, Petermann-Rocha FE, Pham HT, Phillips MR, Pinilla-Monsalve GD, Piradov MA, Plotnikov E, Poddighe D, Polat B, Poluru R, Pond CD, Poudel GR, Pouramini A, Pourbagher-Shahri AM, Pourfridoni M, Pourtaheri N, Prakash PY, Prakash S, Prakash V, Prates EJS, Pritchett N, Purnobasuki H, Qasim NH, Qattea I, Qian G, Radhakrishnan V, Raee P, Raeisi Shahraki H, Rafique I, Raggi A, Raghav PR, Rahati MM, Rahim F, Rahimi Z, Rahimifard M, Rahman MO, Rahman MHU, Rahman M, Rahman MA, Rahmani AM, Rahmani S, Rahmani Youshanlouei H, Rahmati M, Raj Moolambally S, Rajabpour-Sanati A, Ramadan H, Ramasamy SK, Ramasubramani P, Ramazanu S, Rancic N, Rao IR, Rao SJ, Rapaka D, Rashedi V, Rashid AM, Rashidi MM, Rashidi Alavijeh M, Rasouli-Saravani A, Rawaf S, Razo C, Redwan EMM, Rekabi Bana A, Remuzzi G, Rezaei N, Rezaei N, Rezaei N, Rezaeian M, Rhee TG, Riad A, Robinson SR, Rodrigues M, Rodriguez JAB, Roever L, Rogowski ELB, Romoli M, Ronfani L, Roy P, Roy Pramanik K, Rubagotti E, Ruiz MA, Russ TC, S Sunnerhagen K, Saad AMA, Saadatian Z, Saber K, SaberiKamarposhti M, Sacco S, Saddik B, Sadeghi E, Sadeghian S, Saeed U, Saeed U, Safdarian M, Safi SZ, Sagar R, Sagoe D, Saheb Sharif-Askari F, Saheb Sharif-Askari N, Sahebkar A, Sahoo SS, Sahraian MA, Sajedi SA, Sakshaug JW, Saleh MA, Salehi Omran H, Salem MR, Salimi S, Samadi Kafil H, Samadzadeh S, Samargandy S, Samodra YL, Samuel VP, Samy AM, Sanadgol N, Sanjeev RK, Sanmarchi F, Santomauro DF, Santri IN, Santric-Milicevic MM, Saravanan A, Sarveazad A, Satpathy M, Saylan M, Sayyah M, Scarmeas N, Schlaich MP, Schuermans A, Schwarzinger M, Schwebel DC, Selvaraj S, Sendekie AK, Sengupta P, Senthilkumaran S, Serban D, Sergindo MT, Sethi Y, SeyedAlinaghi S, Seylani A, Shabani M, Shabany M, Shafie M, Shahabi S, Shahbandi A, Shahid S, Shahraki-Sanavi F, Shahsavari HR, Shahwan MJ, Shaikh MA, Shaji KS, Sham S, Shama ATT, Shamim MA, Shams-Beyranvand M, Shamsi MA, Shanawaz M, Sharath M, Sharfaei S, Sharifan A, Sharma M, Sharma R, Shashamo BB, Shayan M, Sheikhi RA, Shekhar S, Shen J, Shenoy SM, Shetty PH, Shiferaw DS, Shigematsu M, Shiri R, Shittu A, Shivakumar KM, Shokri F, Shool S, Shorofi SA, Shrestha S, Siankam Tankwanchi AB, Siddig EE, Sigfusdottir ID, Silva JP, Silva LMLR, Sinaei E, Singh BB, Singh G, Singh P, Singh S, Sirota SB, Sivakumar S, Sohag AAM, Solanki R, Soleimani H, Solikhah S, Solomon Y, Solomon Y, Song S, Song Y, Sotoudeh H, Spartalis M, Stark BA, Starnes JR, Starodubova AV, Stein DJ, Steiner TJ, Stovner LJ, Suleman M, Suliankatchi Abdulkader R, Sultana A, Sun J, Sunkersing D, Sunny A, Susianti H, Swain CK, Szeto MD, Tabarés-Seisdedos R, Tabatabaei SM, Tabatabai S, Tabish M, Taheri M, Tahvildari A, Tajbakhsh A, Tampa M, Tamuzi JJLL, Tan KK, Tang H, Tareke M, Tarigan IU, Tat NY, Tat VY, Tavakoli Oliaee R, Tavangar SM, Tavasol A, Tefera YM, Tehrani-Banihashemi A, Temesgen WA, Temsah MH, Teramoto M, Tesfaye AH, Tesfaye EG, Tesler R, Thakali O, Thangaraju P, Thapa R, Thapar R, Thomas NK, Thrift AG, Ticoalu JHV, Tillawi T, Toghroli R, Tonelli M, Tovani-Palone MR, Traini E, Tran NM, Tran NH, Tran PV, Tromans SJ, Truelsen TC, Truyen TTTT, Tsatsakis A, Tsegay GM, Tsermpini EE, Tualeka AR, Tufa DG, Ubah CS, Udoakang AJ, Ulhaq I, Umair M, Umakanthan S, Umapathi KK, Unim B, Unnikrishnan B, Vaithinathan AG, Vakilian A, Valadan Tahbaz S, Valizadeh R, Van den Eynde J, Vart P, Varthya SB, Vasankari TJ, Vaziri S, Vellingiri B, Venketasubramanian N, Verras GI, Vervoort D, Villafañe JH, Villani L, Vinueza Veloz AF, Viskadourou M, Vladimirov SK, Vlassov V, Volovat SR, Vu LT, Vujcic IS, Wagaye B, Waheed Y, Wahood W, Walde MT, Wang F, Wang S, Wang Y, Wang YP, Waqas M, Waris A, Weerakoon KG, Weintraub RG, Weldemariam AH, Westerman R, Whisnant JL, Wickramasinghe DP, Wickramasinghe ND, Willekens B, Wilner LB, Winkler AS, Wolfe CDA, Wu AM, Wulf Hanson S, Xu S, Xu X, Yadollahpour A, Yaghoubi S, Yahya G, Yamagishi K, Yang L, Yano Y, Yao Y, Yehualashet SS, Yeshaneh A, Yesiltepe M, Yi S, Yiğit A, Yiğit V, Yon DK, Yonemoto N, You Y, Younis MZ, Yu C, Yusuf H, Zadey S, Zahedi M, Zakham F, Zaki N, Zali A, Zamagni G, Zand R, Zandieh GGZ, Zangiabadian M, Zarghami A, Zastrozhin MS, Zeariya MGM, Zegeye ZB, Zeukeng F, Zhai C, Zhang C, Zhang H, Zhang Y, Zhang ZJ, Zhao H, Zhao Y, Zheng P, Zhou H, Zhu B, Zhumagaliuly A, Zielińska M, Zikarg YT, Zoladl M, Murray CJL, Ong KL, Feigin VL, Vos T, Dua T. Global, regional, and national burden of disorders affecting the nervous system, 1990-2021: a systematic analysis for the Global Burden of Disease Study 2021. Lancet Neurol 2024; 23:344-381. [PMID: 38493795 PMCID: PMC10949203 DOI: 10.1016/s1474-4422(24)00038-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2023] [Revised: 01/23/2024] [Accepted: 01/26/2024] [Indexed: 03/19/2024]
Abstract
BACKGROUND Disorders affecting the nervous system are diverse and include neurodevelopmental disorders, late-life neurodegeneration, and newly emergent conditions, such as cognitive impairment following COVID-19. Previous publications from the Global Burden of Disease, Injuries, and Risk Factor Study estimated the burden of 15 neurological conditions in 2015 and 2016, but these analyses did not include neurodevelopmental disorders, as defined by the International Classification of Diseases (ICD)-11, or a subset of cases of congenital, neonatal, and infectious conditions that cause neurological damage. Here, we estimate nervous system health loss caused by 37 unique conditions and their associated risk factors globally, regionally, and nationally from 1990 to 2021. METHODS We estimated mortality, prevalence, years lived with disability (YLDs), years of life lost (YLLs), and disability-adjusted life-years (DALYs), with corresponding 95% uncertainty intervals (UIs), by age and sex in 204 countries and territories, from 1990 to 2021. We included morbidity and deaths due to neurological conditions, for which health loss is directly due to damage to the CNS or peripheral nervous system. We also isolated neurological health loss from conditions for which nervous system morbidity is a consequence, but not the primary feature, including a subset of congenital conditions (ie, chromosomal anomalies and congenital birth defects), neonatal conditions (ie, jaundice, preterm birth, and sepsis), infectious diseases (ie, COVID-19, cystic echinococcosis, malaria, syphilis, and Zika virus disease), and diabetic neuropathy. By conducting a sequela-level analysis of the health outcomes for these conditions, only cases where nervous system damage occurred were included, and YLDs were recalculated to isolate the non-fatal burden directly attributable to nervous system health loss. A comorbidity correction was used to calculate total prevalence of all conditions that affect the nervous system combined. FINDINGS Globally, the 37 conditions affecting the nervous system were collectively ranked as the leading group cause of DALYs in 2021 (443 million, 95% UI 378-521), affecting 3·40 billion (3·20-3·62) individuals (43·1%, 40·5-45·9 of the global population); global DALY counts attributed to these conditions increased by 18·2% (8·7-26·7) between 1990 and 2021. Age-standardised rates of deaths per 100 000 people attributed to these conditions decreased from 1990 to 2021 by 33·6% (27·6-38·8), and age-standardised rates of DALYs attributed to these conditions decreased by 27·0% (21·5-32·4). Age-standardised prevalence was almost stable, with a change of 1·5% (0·7-2·4). The ten conditions with the highest age-standardised DALYs in 2021 were stroke, neonatal encephalopathy, migraine, Alzheimer's disease and other dementias, diabetic neuropathy, meningitis, epilepsy, neurological complications due to preterm birth, autism spectrum disorder, and nervous system cancer. INTERPRETATION As the leading cause of overall disease burden in the world, with increasing global DALY counts, effective prevention, treatment, and rehabilitation strategies for disorders affecting the nervous system are needed. FUNDING Bill & Melinda Gates Foundation.
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Ledesma JR, Ma J, Zhang M, Basting AVL, Chu HT, Vongpradith A, Novotney A, LeGrand KE, Xu YY, Dai X, Nicholson SI, Stafford LK, Carter A, Ross JM, Abbastabar H, Abdoun M, Abdulah DM, Aboagye RG, Abolhassani H, Abrha WA, Abubaker Ali H, Abu-Gharbieh E, Aburuz S, Addo IY, Adepoju AV, Adhikari K, Adnani QES, Adra S, Afework A, Aghamiri S, Agyemang-Duah W, Ahinkorah BO, Ahmad D, Ahmad S, Ahmadzade AM, Ahmed H, Ahmed M, Ahmed A, Akinosoglou K, AL-Ahdal TMA, Alam N, Albashtawy M, AlBataineh MT, Al-Gheethi AAS, Ali A, Ali EA, Ali L, Ali Z, Ali SSS, Allel K, Altaf A, Al-Tawfiq JA, Alvis-Guzman N, Alvis-Zakzuk NJ, Amani R, Amusa GA, Amzat J, Andrews JR, Anil A, Anwer R, Aravkin AY, Areda D, Artamonov AA, Aruleba RT, Asemahagn MA, Atre SR, Aujayeb A, Azadi D, Azadnajafabad S, Azzam AY, Badar M, Badiye AD, Bagherieh S, Bahadorikhalili S, Baig AA, Banach M, Banik B, Bardhan M, Barqawi HJ, Basharat Z, Baskaran P, Basu S, Beiranvand M, Belete MA, Belew MA, Belgaumi UI, Beloukas A, Bettencourt PJG, Bhagavathula AS, Bhardwaj N, Bhardwaj P, Bhargava A, Bhat V, Bhatti JS, Bhatti GK, Bikbov B, Bitra VR, Bjegovic-Mikanovic V, Buonsenso D, Burkart K, Bustanji Y, Butt ZA, Camargos P, Cao Y, Carr S, Carvalho F, Cegolon L, Cenderadewi M, Cevik M, Chahine Y, Chattu VK, Ching PR, Chopra H, Chung E, Claassens MM, Coberly K, Cruz-Martins N, Dabo B, Dadana S, Dadras O, Darban I, Darega Gela J, Darwesh AM, Dashti M, Demessa BH, Demisse B, Demissie S, Derese AMA, Deribe K, Desai HD, Devanbu VGC, Dhali A, Dhama K, Dhingra S, Do THP, Dongarwar D, Dsouza HL, Dube J, Dziedzic AM, Ed-Dra A, Efendi F, Effendi DE, Eftekharimehrabad A, Ekadinata N, Ekundayo TC, Elhadi M, Elilo LT, Emeto TI, Engelbert Bain L, Fagbamigbe AF, Fahim A, Feizkhah A, Fetensa G, Fischer F, Gaipov A, Gandhi AP, Gautam RK, Gebregergis MW, Gebrehiwot M, Gebrekidan KG, Ghaffari K, Ghassemi F, Ghazy RM, Goodridge A, Goyal A, Guan SY, Gudeta MD, Guled RA, Gultom NB, Gupta VB, Gupta VK, Gupta S, Hagins H, Hailu SG, Hailu WB, Hamidi S, Hanif A, Harapan H, Hasan RS, Hassan S, Haubold J, Hezam K, Hong SH, Horita N, Hossain MB, Hosseinzadeh M, Hostiuc M, Hostiuc S, Huynh HH, Ibitoye SE, Ikuta KS, Ilic IM, Ilic MD, Islam MR, Ismail NE, Ismail F, Jafarzadeh A, Jakovljevic M, Jalili M, Janodia MD, Jomehzadeh N, Jonas JB, Joseph N, Joshua CE, Kabir Z, Kamble BD, Kanchan T, Kandel H, Kanmodi KK, Kantar RS, Karaye IM, Karimi Behnagh A, Kassa GG, Kaur RJ, Kaur N, Khajuria H, Khamesipour F, Khan YH, Khan MN, Khan Suheb MZ, Khatab K, Khatami F, Kim MS, Kosen S, Koul PA, Koulmane Laxminarayana SL, Krishan K, Kucuk Bicer B, Kuddus MA, Kulimbet M, Kumar N, Lal DK, Landires I, Latief K, Le TDT, Le TTT, Ledda C, Lee M, Lee SW, Lerango TL, Lim SS, Liu C, Liu X, Lopukhov PD, Luo H, Lv H, Mahajan PB, Mahboobipour AA, Majeed A, Malakan Rad E, Malhotra K, Malik MSA, Malinga LA, Mallhi TH, Manilal A, Martinez-Guerra BA, Martins-Melo FR, Marzo RR, Masoumi-Asl H, Mathur V, Maude RJ, Mehrotra R, Memish ZA, Mendoza W, Menezes RG, Merza MA, Mestrovic T, Mhlanga L, Misra S, Misra AK, Mithra P, Moazen B, Mohammed H, Mokdad AH, Monasta L, Moore CE, Mousavi P, Mulita F, Musaigwa F, Muthusamy R, Nagarajan AJ, Naghavi P, Naik GR, Naik G, Nair S, Nair TS, Natto ZS, Nayak BP, Negash H, Nguyen DH, Nguyen VT, Niazi RK, Nnaji CA, Nnyanzi LA, Noman EA, Nomura S, Oancea B, Obamiro KO, Odetokun IA, Odo DBO, Odukoya OO, Oh IH, Okereke CO, Okonji OC, Oren E, Ortiz-Brizuela E, Osuagwu UL, Ouyahia A, P A MP, Parija PP, Parikh RR, Park S, Parthasarathi A, Patil S, Pawar S, Peng M, Pepito VCF, Peprah P, Perdigão J, Perico N, Pham HT, Postma MJ, Prabhu ARA, Prasad M, Prashant A, Prates EJS, Rahim F, Rahman M, Rahman MA, Rahmati M, Rajaa S, Ramasamy SK, Rao IR, Rao SJ, Rapaka D, Rashid AM, Ratan ZA, Ravikumar N, Rawaf S, Reddy MMRK, Redwan EMM, Remuzzi G, Reyes LF, Rezaei N, Rezaeian M, Rezahosseini O, Rodrigues M, Roy P, Ruela GDA, Sabour S, Saddik B, Saeed U, Safi SZ, Saheb Sharif-Askari N, Saheb Sharif-Askari F, Sahebkar A, Sahiledengle B, Sahoo SS, Salam N, Salami AA, Saleem S, Saleh MA, Samadi Kafil H, Samadzadeh S, Samodra YL, Sanjeev RK, Saravanan A, Sawyer SM, Selvaraj S, Senapati S, Senthilkumaran S, Shah PA, Shahid S, Shaikh MA, Sham S, Shamshirgaran MA, Shanawaz M, Sharath M, Sherchan SP, Shetty RS, Shirzad-Aski H, Shittu A, Siddig EE, Silva JP, Singh S, Singh P, Singh H, Singh JA, Siraj MS, Siswanto S, Solanki R, Solomon Y, Soriano JB, Sreeramareddy CT, Srivastava VK, Steiropoulos P, Swain CK, Tabuchi T, Tampa M, Tamuzi JJLL, Tat NY, Tavakoli Oliaee R, Teklay G, Tesfaye EG, Tessema B, Thangaraju P, Thapar R, Thum CCC, Ticoalu JHV, Tleyjeh IM, Tobe-Gai R, Toma TM, Tram KH, Udoakang AJ, Umar TP, Umeokonkwo CD, Vahabi SM, Vaithinathan AG, van Boven JFM, Varthya SB, Wang Z, Warsame MSA, Westerman R, Wonde TE, Yaghoubi S, Yi S, Yiğit V, Yon DK, Yonemoto N, Yu C, Zakham F, Zangiabadian M, Zeukeng F, Zhang H, Zhao Y, Zheng P, Zielińska M, Salomon JA, Reiner Jr RC, Naghavi M, Vos T, Hay SI, Murray CJL, Kyu HH. Global, regional, and national age-specific progress towards the 2020 milestones of the WHO End TB Strategy: a systematic analysis for the Global Burden of Disease Study 2021. Lancet Infect Dis 2024:S1473-3099(24)00007-0. [PMID: 38518787 DOI: 10.1016/s1473-3099(24)00007-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/23/2023] [Revised: 12/09/2023] [Accepted: 01/08/2024] [Indexed: 03/24/2024]
Abstract
BACKGROUND Global evaluations of the progress towards the WHO End TB Strategy 2020 interim milestones on mortality (35% reduction) and incidence (20% reduction) have not been age specific. We aimed to assess global, regional, and national-level burdens of and trends in tuberculosis and its risk factors across five separate age groups, from 1990 to 2021, and to report on age-specific progress between 2015 and 2020. METHODS We used the Global Burden of Diseases, Injuries, and Risk Factors Study 2021 (GBD 2021) analytical framework to compute age-specific tuberculosis mortality and incidence estimates for 204 countries and territories (1990-2021 inclusive). We quantified tuberculosis mortality among individuals without HIV co-infection using 22 603 site-years of vital registration data, 1718 site-years of verbal autopsy data, 825 site-years of sample-based vital registration data, 680 site-years of mortality surveillance data, and 9 site-years of minimally invasive tissue sample (MITS) diagnoses data as inputs into the Cause of Death Ensemble modelling platform. Age-specific HIV and tuberculosis deaths were established with a population attributable fraction approach. We analysed all available population-based data sources, including prevalence surveys, annual case notifications, tuberculin surveys, and tuberculosis mortality, in DisMod-MR 2.1 to produce internally consistent age-specific estimates of tuberculosis incidence, prevalence, and mortality. We also estimated age-specific tuberculosis mortality without HIV co-infection that is attributable to the independent and combined effects of three risk factors (smoking, alcohol use, and diabetes). As a secondary analysis, we examined the potential impact of the COVID-19 pandemic on tuberculosis mortality without HIV co-infection by comparing expected tuberculosis deaths, modelled with trends in tuberculosis deaths from 2015 to 2019 in vital registration data, with observed tuberculosis deaths in 2020 and 2021 for countries with available cause-specific mortality data. FINDINGS We estimated 9·40 million (95% uncertainty interval [UI] 8·36 to 10·5) tuberculosis incident cases and 1·35 million (1·23 to 1·52) deaths due to tuberculosis in 2021. At the global level, the all-age tuberculosis incidence rate declined by 6·26% (5·27 to 7·25) between 2015 and 2020 (the WHO End TB strategy evaluation period). 15 of 204 countries achieved a 20% decrease in all-age tuberculosis incidence between 2015 and 2020, eight of which were in western sub-Saharan Africa. When stratified by age, global tuberculosis incidence rates decreased by 16·5% (14·8 to 18·4) in children younger than 5 years, 16·2% (14·2 to 17·9) in those aged 5-14 years, 6·29% (5·05 to 7·70) in those aged 15-49 years, 5·72% (4·02 to 7·39) in those aged 50-69 years, and 8·48% (6·74 to 10·4) in those aged 70 years and older, from 2015 to 2020. Global tuberculosis deaths decreased by 11·9% (5·77 to 17·0) from 2015 to 2020. 17 countries attained a 35% reduction in deaths due to tuberculosis between 2015 and 2020, most of which were in eastern Europe (six countries) and central Europe (four countries). There was variable progress by age: a 35·3% (26·7 to 41·7) decrease in tuberculosis deaths in children younger than 5 years, a 29·5% (25·5 to 34·1) decrease in those aged 5-14 years, a 15·2% (10·0 to 20·2) decrease in those aged 15-49 years, a 7·97% (0·472 to 14·1) decrease in those aged 50-69 years, and a 3·29% (-5·56 to 9·07) decrease in those aged 70 years and older. Removing the combined effects of the three attributable risk factors would have reduced the number of all-age tuberculosis deaths from 1·39 million (1·28 to 1·54) to 1·00 million (0·703 to 1·23) in 2020, representing a 36·5% (21·5 to 54·8) reduction in tuberculosis deaths compared to those observed in 2015. 41 countries were included in our analysis of the impact of the COVID-19 pandemic on tuberculosis deaths without HIV co-infection in 2020, and 20 countries were included in the analysis for 2021. In 2020, 50 900 (95% CI 49 700 to 52 400) deaths were expected across all ages, compared to an observed 45 500 deaths, corresponding to 5340 (4070 to 6920) fewer deaths; in 2021, 39 600 (38 300 to 41 100) deaths were expected across all ages compared to an observed 39 000 deaths, corresponding to 657 (-713 to 2180) fewer deaths. INTERPRETATION Despite accelerated progress in reducing the global burden of tuberculosis in the past decade, the world did not attain the first interim milestones of the WHO End TB Strategy in 2020. The pace of decline has been unequal with respect to age, with older adults (ie, those aged >50 years) having the slowest progress. As countries refine their national tuberculosis programmes and recalibrate for achieving the 2035 targets, they could consider learning from the strategies of countries that achieved the 2020 milestones, as well as consider targeted interventions to improve outcomes in older age groups. FUNDING Bill & Melinda Gates Foundation.
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Ziegenfuß C, van Landeghem N, Meier C, Pförtner R, Eckstein A, Dammann P, Haubold P, Haubold J, Forsting M, Deuschl C, Wanke I, Li Y. MR Imaging Characteristics of Solitary Fibrous Tumors of the Orbit : Case Series of 18 Patients. Clin Neuroradiol 2024:10.1007/s00062-024-01400-8. [PMID: 38456912 DOI: 10.1007/s00062-024-01400-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2024] [Accepted: 02/14/2024] [Indexed: 03/09/2024]
Abstract
PURPOSE Solitary fibrous tumor (SFT) of the orbit is a rare tumor that was first described in 1994. We aimed to investigate its imaging characteristics that may facilitate the differential diagnosis between SFT and other types of orbital tumors. MATERIAL AND METHODS Magnetic resonance imaging (MRI) data of patients with immunohistochemically confirmed orbital SFT from 2002 to 2022 at a tertiary care center were retrospectively analyzed. Tumor location, size, morphological characteristics, and contrast enhancement features were evaluated. RESULTS Of the 18 eligible patients 10 were female (56%) with a mean age of 52 years. Most of the SFTs were oval-shaped (67%) with a sharp margin (83%). The most frequent locations were the laterocranial quadrant (44%), the extraconal space (67%) and the dorsal half of the orbit (67%). A flow void phenomenon was observed in nearly all cases (94%). On the T1-weighted imaging, tumor signal intensity (SI) was significantly lower than that of the retrobulbar fat and appeared predominantly equivalent (82%) to the temporomesial brain cortex, while on T2-weighted imaging its SI remained equivalent (50%) or slightly hyperintense to that of brain cortex. More than half of the lesions showed a homogeneous contrast enhancement pattern with a median SI increase of 2.2-fold compared to baseline precontrast imaging. CONCLUSION The SFT represents a rare orbital tumor with several characteristic imaging features. It was mostly oval-shaped with a sharp margin and frequently localized in the extraconal space and dorsal half of the orbit. Flow voids indicating hypervascularization were the most common findings.
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Affiliation(s)
- Christoph Ziegenfuß
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Hufelandstraße 55, 45147, Essen, Germany.
| | - Natalie van Landeghem
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Hufelandstraße 55, 45147, Essen, Germany
| | - Chiara Meier
- Department of Oral and Maxillofacial Surgery, University Hospital Essen, Kliniken-Essen-Mitte, Henricistraße 92, 45136, Essen, Germany
| | - Roman Pförtner
- Department of Oral and Maxillofacial Surgery, University Hospital Essen, Kliniken-Essen-Mitte, Henricistraße 92, 45136, Essen, Germany
| | - Anja Eckstein
- Department of Ophthalmology, University Hospital Essen, Hufelandstraße 55, 45147, Essen, Germany
| | - Philipp Dammann
- Department of Neurosurgery and Spine Surgery, University Hospital Essen, Hufelandstraße 55, 45147, Essen, Germany
| | - Patrizia Haubold
- Department of Diagnostic and Interventional Radiology, Kliniken Essen-Mitte, Henricistraße 92, 45136, Essen, Germany
| | - Johannes Haubold
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Hufelandstraße 55, 45147, Essen, Germany
| | - Michael Forsting
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Hufelandstraße 55, 45147, Essen, Germany
| | - Cornelius Deuschl
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Hufelandstraße 55, 45147, Essen, Germany
| | - Isabel Wanke
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Hufelandstraße 55, 45147, Essen, Germany
- Swiss Neuroradiology Institute, Bürglistraße 29, 8002, Zürich, Switzerland
| | - Yan Li
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Hufelandstraße 55, 45147, Essen, Germany
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Eshetie TC, Eskandarieh S, Espinosa-Montero J, Estep K, Etaee F, Eze UA, Fabin N, Fadaka AO, Fagbamigbe AF, Fahimi S, Falzone L, Farinha CSES, Faris MEM, Farjoud Kouhanjani M, Faro A, Farrokhpour H, Fatehizadeh A, Fattahi H, Fauk NK, Fazeli P, Feigin VL, Fekadu G, Fereshtehnejad SM, Feroze AH, Ferrante D, Ferrara P, Ferreira N, Fetensa G, Filip I, Fischer F, Flavel J, Flaxman AD, Flor LS, Florin BT, Folayan MO, Foley KM, Fomenkov AA, Force LM, Fornari C, Foroutan B, Foschi M, Francis KL, Franklin RC, Freitas A, Friedman J, Friedman SD, Fukumoto T, Fuller JE, Gaal PA, Gadanya MA, Gaihre S, Gaipov A, Gakidou E, Galali Y, Galehdar N, Gallus S, Gan Q, Gandhi AP, Ganesan B, Garg J, Gau SY, Gautam P, Gautam RK, Gazzelloni F, Gebregergis MW, Gebrehiwot M, Gebremariam TB, Gerema U, Getachew ME, Getachew T, Gething PW, Ghafourifard M, Ghahramani S, Ghailan KY, Ghajar A, Ghanbarnia MJ, Ghasemi M, Ghasemzadeh A, Ghassemi F, Ghazy RM, Ghimire S, Gholamian A, Gholamrezanezhad A, Ghorbani Vajargah 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Heidari-Soureshjani R, Helfer B, Herteliu C, Hesami H, Hettiarachchi D, Heyi DZ, Hezam K, Hiraike Y, Hoffman HJ, Holla R, Horita N, Hossain MB, Hossain MM, Hossain S, Hosseini MS, Hosseinzadeh H, Hosseinzadeh M, Hostiuc M, Hostiuc S, Hsairi M, Hsieh VCR, Hu C, Huang J, Huda MN, Hugo FN, Hultström M, Hussain J, Hussain S, Hussein NR, Huy LD, Huynh HH, Hwang BF, Ibitoye SE, Idowu OO, Ijo D, Ikuta KS, Ilaghi M, Ilesanmi OS, Ilic IM, Ilic MD, Immurana M, Inbaraj LR, Iradukunda A, Iravanpour F, Iregbu KC, Islam MR, Islam MM, Islam SMS, Islami F, Ismail NE, Isola G, Iwagami M, Iwu CCD, Iwu-Jaja CJ, Iyer M, J LM, Jaafari J, Jacob L, Jacobsen KH, Jadidi-Niaragh F, Jafarinia M, Jaggi K, Jahankhani K, Jahanmehr N, Jahrami H, Jain A, Jain N, Jairoun AA, Jakovljevic M, Jalilzadeh Yengejeh R, Jamshidi E, Jani CT, Janko MM, Jatau AI, Jayapal SK, Jayaram S, Jeganathan J, Jema AT, Jemere DM, Jeong W, Jha AK, Jha RP, Ji JS, Jiang H, Jin Y, Jin Y, Johnson O, Jomehzadeh N, Jones DP, Joo T, Joseph A, Joseph N, Joshua CE, Jozwiak JJ, Jürisson M, Kaambwa B, Kabir A, Kabir H, Kabir Z, Kadashetti V, Kahe F, Kakodkar PV, Kalani R, Kalankesh LR, Kaliyadan F, Kalra S, Kamath A, Kamireddy A, Kanagasabai T, Kandel H, Kanmiki EW, Kanmodi KK, Kantar RS, Kapoor N, Karajizadeh M, Karami Matin B, Karanth SD, Karaye IM, Karim A, Karimi H, Karimi SE, Karimi Behnagh A, Karkhah S, Karna AK, Kashoo FZ, Kasraei H, Kassaw NA, Kassebaum NJ, Kassel MB, Katamreddy A, Katikireddi SV, Katoto PDMC, Kauppila JH, Kaur N, Kaydi N, Kayibanda JF, Kayode GA, Kazemi F, Kazemian S, Kazeminia S, Keikavoosi-Arani L, Keller C, Kempen JH, Kerr JA, Kesse-Guyot E, Keykhaei M, Khadembashiri MM, Khadembashiri MA, Khafaie MA, Khajuria H, Khalafi M, Khalaji A, Khalid N, Khalil IA, Khamesipour F, Khan A, Khan G, Khan I, Khan IA, Khan M, Khan MAB, Khan T, Khan suheb MZ, Khanmohammadi S, Khatab K, Khatami F, Khavandegar A, Khayat Kashani HR, Kheirallah KA, Khidri FF, Khodadoust E, Khormali M, Khosrowjerdi M, Khubchandani J, 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Norouzian Baghani A, Norrving B, Noubiap JJ, Novotney A, Nri-Ezedi CA, Ntaios G, Ntsekhe M, Nuñez-Samudio V, Nurrika D, Oancea B, Obamiro KO, Odetokun IA, Ofakunrin AOD, Ogunsakin RE, Oguta JO, Oh IH, Okati-Aliabad H, Okeke SR, Okekunle AP, Okidi L, Okonji OC, Okwute PG, Olagunju AT, Olaiya MT, Olanipekun TO, Olatubi MI, Olivas-Martinez A, Oliveira GMM, Oliver S, Olorukooba AA, Olufadewa II, Olusanya BO, Olusanya JO, Oluwafemi YD, Oluwatunase GO, Omar HA, Omer GL, Ong S, Onwujekwe OE, Onyedibe KI, Opio JN, Ordak M, Orellana ER, Orisakwe OE, Orish VN, Orru H, Ortega-Altamirano DV, Ortiz A, Ortiz-Brizuela E, Ortiz-Prado E, Osuagwu UL, Otoiu A, Otstavnov N, Ouyahia A, Ouyang G, Owolabi MO, Oyeyemi IT, Oyeyemi OT, Ozten Y, P A MP, Padubidri JR, Pahlavikhah Varnosfaderani M, Pal PK, Palicz T, Palladino C, Palladino R, Palma-Alvarez RF, Pana A, Panahi P, Pandey A, Pandi-Perumal SR, Pando-Robles V, Pangaribuan HU, Panos GD, Pantazopoulos I, Papadopoulou P, Pardhan S, Parikh RR, Park S, Parthasarathi A, Pashaei A, Pasupula DK, Patel JR, Patel SK, Pathan AR, Patil A, Patil S, Patoulias D, Patthipati VS, Paudel U, Pawar S, Pazoki Toroudi H, Pease SA, Peden AE, Pedersini P, Peng M, Pensato U, Pepito VCF, Peprah EK, Pereira G, Pereira J, Pereira M, Peres MFP, Perianayagam A, Perico N, Petcu IR, Petermann-Rocha FE, Pezzani R, Pham HT, Phillips MR, Pierannunzio D, Pigeolet M, Pigott DM, Pilgrim T, Pinheiro M, Piradov MA, Plakkal N, Plotnikov E, Poddighe D, Pollner P, Poluru R, Pond CD, Postma MJ, Poudel GR, Poudel L, Pourali G, Pourtaheri N, Prada SI, Pradhan PMS, Prajapati VK, Prakash V, Prasad CP, Prasad M, Prashant A, Prates EJS, Purnobasuki H, Purohit BM, Puvvula J, Qaisar R, Qasim NH, Qattea I, Qian G, Quan NK, Radfar A, Radhakrishnan V, Raee P, Raeisi Shahraki H, Rafiei Alavi SN, Rafique I, Raggi A, Rahim F, Rahman MM, Rahman M, Rahman MA, Rahman T, Rahmani AM, Rahmani S, Rahnavard N, Rai P, Rajaa S, Rajabpour-Sanati A, Rajput P, Ram P, Ramadan H, Ramasamy SK, Ramazanu S, Rana J, Rana K, Ranabhat CL, Rancic N, Rani S, Ranjan S, Rao CR, Rao IR, Rao M, Rao SJ, Rasali DP, Rasella D, Rashedi S, Rashedi V, Rashid AM, Rasouli-Saravani A, Rastogi P, Rasul A, Ravangard R, Ravikumar N, Rawaf DL, Rawaf S, Rawassizadeh R, Razeghian-Jahromi I, Reddy MMRK, Redwan EMM, Rehman FU, Reiner Jr RC, Remuzzi G, Reshmi B, Resnikoff S, Reyes LF, Rezaee M, Rezaei N, Rezaei N, Rezaeian M, Riaz MA, Ribeiro AI, Ribeiro DC, Rickard J, Rios-Blancas MJ, Robinson-Oden HE, Rodrigues M, Rodriguez JAB, Roever L, Rohilla R, Rohloff P, Romadlon DS, Ronfani L, Roshandel G, Roshanzamir S, Rostamian M, Roy B, Roy P, Rubagotti E, Rumisha SF, Rwegerera GM, Rynkiewicz A, S M, S N C, S Sunnerhagen K, Saad AMA, Sabbatucci M, Saber K, Saber-Ayad MM, Sacco S, Saddik B, Saddler A, Sadee BA, Sadeghi E, Sadeghi M, Sadeghian S, Saeed U, Saeedi M, Safi S, Sagar R, Saghazadeh A, Saheb Sharif-Askari N, Sahoo SS, Sahraian MA, Sajedi SA, Sajid MR, Sakshaug JW, Salahi S, Salahi S, Salamati P, Salami AA, Salaroli LB, Saleh MA, Salehi S, Salem MR, Salem MZY, Salimi S, Samadi Kafil H, Samadzadeh S, Samara KA, Samargandy S, Samodra YL, Samuel VP, Samy AM, Sanabria J, Sanadgol N, Sanganyado E, Sanjeev RK, Sanmarchi F, Sanna F, Santri IN, Santric-Milicevic MM, Sarasmita MA, Saravanan A, Saravi B, Sarikhani Y, Sarkar C, Sarmiento-Suárez R, Sarode GS, Sarode SC, Sarveazad A, Sathian B, Sathish T, Sattin D, Saulam J, Sawyer SM, Saxena S, Saya GK, Sayadi Y, Sayeed A, Sayeed MA, Saylan M, Scarmeas N, Schaarschmidt BM, Schlee W, Schmidt MI, Schuermans A, Schwebel DC, Schwendicke F, Šekerija M, Selvaraj S, Semreen MH, Senapati S, Sengupta P, Senthilkumaran S, Sepanlou SG, Serban D, Sertsu A, Sethi Y, SeyedAlinaghi S, Seyedi SA, Shafaat A, Shafaat O, Shafie M, Shafiee A, Shah NS, Shah PA, Shahabi S, Shahbandi A, Shahid I, Shahid S, Shahid W, Shahwan MJ, Shaikh MA, Shakeri A, Shakil H, Sham S, Shamim MA, Shams-Beyranvand M, Shamshad H, Shamshirgaran MA, Shamsi MA, Shanawaz M, Shankar A, Sharfaei S, Sharifan A, Shariff M, Sharifi-Rad J, Sharma M, Sharma R, Sharma S, Sharma V, Shastry RP, Shavandi A, Shaw DH, Shayan AM, Shehabeldine AME, Sheikh A, Sheikhi RA, Shen J, Shenoy MM, Shetty BSK, Shetty RS, Shey RA, Shiani A, Shibuya K, Shiferaw D, Shigematsu M, Shin JI, Shin MJ, Shiri R, Shirkoohi R, Shittu A, Shiue I, Shivakumar KM, Shivarov V, Shool S, Shrestha S, Shuja KH, Shuval K, Si Y, Sibhat MM, Siddig EE, Sigfusdottir ID, Silva JP, Silva LMLR, Silva S, Simões JP, Simpson CR, Singal A, Singh A, Singh A, Singh A, Singh BB, Singh B, Singh M, Singh M, Singh NP, Singh P, Singh S, Siraj MS, Sitas F, Sivakumar S, Skryabin VY, Skryabina AA, Sleet DA, Slepak ELN, Sohrabi H, Soleimani H, Soliman SSM, Solmi M, Solomon Y, Song Y, Sorensen RJD, Soriano JB, Soyiri IN, Spartalis M, Sreeramareddy CT, Starnes JR, Starodubov VI, Starodubova AV, Stefan SC, Stein DJ, Steinbeis F, Steiropoulos P, Stockfelt L, Stokes MA, Stortecky S, Stranges S, Stroumpoulis K, Suleman M, Suliankatchi Abdulkader R, Sultana A, Sun J, Sunkersing D, Susanty S, Swain CK, Sykes BL, Szarpak L, Szeto MD, Szócska M, Tabaee Damavandi P, Tabatabaei Malazy O, Tabatabaeizadeh SA, Tabatabai S, Tabb KM, Tabish M, Taborda-Barata LM, Tabuchi T, Tadesse BT, Taheri A, Taheri Abkenar Y, Taheri Soodejani M, Taherkhani A, Taiba J, Tajbakhsh A, Talaat IM, Talukder A, Tamuzi JL, Tan KK, Tang H, Tang HK, Tat NY, Tat VY, Tavakoli Oliaee R, Tavangar SM, Taveira N, Tebeje TM, Tefera YM, Teimoori M, Temsah MH, Temsah RMH, Teramoto M, Tesfaye SH, Thangaraju P, Thankappan KR, Thapa R, Thapar R, Thomas N, Thrift AG, Thum CCC, Tian J, Tichopad A, Ticoalu JHV, Tiruye TY, Tohidast SA, Tonelli M, Touvier M, Tovani-Palone MR, Tram KH, Tran NM, Trico D, Trihandini I, Tromans SJ, Truong VT, Truyen TTTT, Tsermpini EE, Tumurkhuu M, Tung K, Tyrovolas S, Ubah CS, Udoakang AJ, Udoh A, Ulhaq I, Ullah S, Ullah S, Umair M, Umar TP, Umeokonkwo CD, Umesh A, Unim B, Unnikrishnan B, Upadhyay E, Urso D, Vacante M, Vahdani AM, Vaithinathan AG, Valadan Tahbaz S, Valizadeh R, Van den Eynde J, Varavikova E, Varga O, Varma SA, Vart P, Varthya SB, Vasankari TJ, Veerman LJ, Venketasubramanian N, Venugopal D, Verghese NA, Verma M, Verma P, Veroux M, Verras GI, Vervoort D, Vieira RJ, Villafañe JH, Villani L, Villanueva GI, Villeneuve PJ, Violante FS, Visontay R, Vlassov V, Vo B, Vollset SE, Volovat SR, Volovici V, Vongpradith A, Vos T, Vujcic IS, Vukovic R, Wado YD, Wafa HA, Waheed Y, Wamai RG, Wang C, Wang D, Wang F, Wang S, Wang S, Wang Y, Wang YP, Ward P, Watson S, Weaver MR, Weerakoon KG, Weiss DJ, Weldemariam AH, Wells KM, Wen YF, Werdecker A, Westerman R, Wickramasinghe DP, Wickramasinghe ND, Wijeratne T, Wilson S, Wojewodzic MW, Wool EE, Woolf AD, Wu D, Wulandari RD, Xiao H, Xu B, Xu X, Yadav L, Yaghoubi S, Yang L, Yano Y, Yao Y, Ye P, Yesera GE, Yesodharan R, Yesuf SA, Yiğit A, Yiğit V, Yip P, Yon DK, Yonemoto N, You Y, Younis MZ, Yu C, Zadey S, Zadnik V, Zafari N, Zahedi M, Zahid MN, Zahir M, Zakham F, Zaki N, Zakzuk J, Zamagni G, Zaman BA, Zaman SB, Zamora N, Zand R, Zandi M, Zandieh GGZ, Zanghì A, Zare I, Zastrozhin MS, Zeariya MGM, Zeng Y, Zhai C, Zhang C, Zhang H, Zhang H, Zhang Y, Zhang Z, Zhang Z, Zhao H, Zhao Y, Zhao Y, Zheng P, Zhong C, Zhou J, Zhu B, Zhu Z, Ziaeefar P, Zielińska M, Zou Z, Zumla A, Zweck E, Zyoud SH, Lim SS, Murray CJL. Global age-sex-specific mortality, life expectancy, and population estimates in 204 countries and territories and 811 subnational locations, 1950-2021, and the impact of the COVID-19 pandemic: a comprehensive demographic analysis for the Global Burden of Disease Study 2021. Lancet 2024:S0140-6736(24)00476-8. [PMID: 38484753 DOI: 10.1016/s0140-6736(24)00476-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/28/2023] [Revised: 12/08/2023] [Accepted: 03/06/2024] [Indexed: 04/13/2024]
Abstract
BACKGROUND Estimates of demographic metrics are crucial to assess levels and trends of population health outcomes. The profound impact of the COVID-19 pandemic on populations worldwide has underscored the need for timely estimates to understand this unprecedented event within the context of long-term population health trends. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 provides new demographic estimates for 204 countries and territories and 811 additional subnational locations from 1950 to 2021, with a particular emphasis on changes in mortality and life expectancy that occurred during the 2020-21 COVID-19 pandemic period. METHODS 22 223 data sources from vital registration, sample registration, surveys, censuses, and other sources were used to estimate mortality, with a subset of these sources used exclusively to estimate excess mortality due to the COVID-19 pandemic. 2026 data sources were used for population estimation. Additional sources were used to estimate migration; the effects of the HIV epidemic; and demographic discontinuities due to conflicts, famines, natural disasters, and pandemics, which are used as inputs for estimating mortality and population. Spatiotemporal Gaussian process regression (ST-GPR) was used to generate under-5 mortality rates, which synthesised 30 763 location-years of vital registration and sample registration data, 1365 surveys and censuses, and 80 other sources. ST-GPR was also used to estimate adult mortality (between ages 15 and 59 years) based on information from 31 642 location-years of vital registration and sample registration data, 355 surveys and censuses, and 24 other sources. Estimates of child and adult mortality rates were then used to generate life tables with a relational model life table system. For countries with large HIV epidemics, life tables were adjusted using independent estimates of HIV-specific mortality generated via an epidemiological analysis of HIV prevalence surveys, antenatal clinic serosurveillance, and other data sources. Excess mortality due to the COVID-19 pandemic in 2020 and 2021 was determined by subtracting observed all-cause mortality (adjusted for late registration and mortality anomalies) from the mortality expected in the absence of the pandemic. Expected mortality was calculated based on historical trends using an ensemble of models. In location-years where all-cause mortality data were unavailable, we estimated excess mortality rates using a regression model with covariates pertaining to the pandemic. Population size was computed using a Bayesian hierarchical cohort component model. Life expectancy was calculated using age-specific mortality rates and standard demographic methods. Uncertainty intervals (UIs) were calculated for every metric using the 25th and 975th ordered values from a 1000-draw posterior distribution. FINDINGS Global all-cause mortality followed two distinct patterns over the study period: age-standardised mortality rates declined between 1950 and 2019 (a 62·8% [95% UI 60·5-65·1] decline), and increased during the COVID-19 pandemic period (2020-21; 5·1% [0·9-9·6] increase). In contrast with the overall reverse in mortality trends during the pandemic period, child mortality continued to decline, with 4·66 million (3·98-5·50) global deaths in children younger than 5 years in 2021 compared with 5·21 million (4·50-6·01) in 2019. An estimated 131 million (126-137) people died globally from all causes in 2020 and 2021 combined, of which 15·9 million (14·7-17·2) were due to the COVID-19 pandemic (measured by excess mortality, which includes deaths directly due to SARS-CoV-2 infection and those indirectly due to other social, economic, or behavioural changes associated with the pandemic). Excess mortality rates exceeded 150 deaths per 100 000 population during at least one year of the pandemic in 80 countries and territories, whereas 20 nations had a negative excess mortality rate in 2020 or 2021, indicating that all-cause mortality in these countries was lower during the pandemic than expected based on historical trends. Between 1950 and 2021, global life expectancy at birth increased by 22·7 years (20·8-24·8), from 49·0 years (46·7-51·3) to 71·7 years (70·9-72·5). Global life expectancy at birth declined by 1·6 years (1·0-2·2) between 2019 and 2021, reversing historical trends. An increase in life expectancy was only observed in 32 (15·7%) of 204 countries and territories between 2019 and 2021. The global population reached 7·89 billion (7·67-8·13) people in 2021, by which time 56 of 204 countries and territories had peaked and subsequently populations have declined. The largest proportion of population growth between 2020 and 2021 was in sub-Saharan Africa (39·5% [28·4-52·7]) and south Asia (26·3% [9·0-44·7]). From 2000 to 2021, the ratio of the population aged 65 years and older to the population aged younger than 15 years increased in 188 (92·2%) of 204 nations. INTERPRETATION Global adult mortality rates markedly increased during the COVID-19 pandemic in 2020 and 2021, reversing past decreasing trends, while child mortality rates continued to decline, albeit more slowly than in earlier years. Although COVID-19 had a substantial impact on many demographic indicators during the first 2 years of the pandemic, overall global health progress over the 72 years evaluated has been profound, with considerable improvements in mortality and life expectancy. Additionally, we observed a deceleration of global population growth since 2017, despite steady or increasing growth in lower-income countries, combined with a continued global shift of population age structures towards older ages. These demographic changes will likely present future challenges to health systems, economies, and societies. The comprehensive demographic estimates reported here will enable researchers, policy makers, health practitioners, and other key stakeholders to better understand and address the profound changes that have occurred in the global health landscape following the first 2 years of the COVID-19 pandemic, and longer-term trends beyond the pandemic. FUNDING Bill & Melinda Gates Foundation.
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Baldini G, Hosch R, Schmidt CS, Borys K, Kroll L, Koitka S, Haubold P, Pelka O, Nensa F, Haubold J. Addressing the Contrast Media Recognition Challenge: A Fully Automated Machine Learning Approach for Predicting Contrast Phases in CT Imaging. Invest Radiol 2024:00004424-990000000-00203. [PMID: 38436405 DOI: 10.1097/rli.0000000000001071] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/05/2024]
Abstract
OBJECTIVES Accurately acquiring and assigning different contrast-enhanced phases in computed tomography (CT) is relevant for clinicians and for artificial intelligence orchestration to select the most appropriate series for analysis. However, this information is commonly extracted from the CT metadata, which is often wrong. This study aimed at developing an automatic pipeline for classifying intravenous (IV) contrast phases and additionally for identifying contrast media in the gastrointestinal tract (GIT). MATERIALS AND METHODS This retrospective study used 1200 CT scans collected at the investigating institution between January 4, 2016 and September 12, 2022, and 240 CT scans from multiple centers from The Cancer Imaging Archive for external validation. The open-source segmentation algorithm TotalSegmentator was used to identify regions of interest (pulmonary artery, aorta, stomach, portal/splenic vein, liver, portal vein/hepatic veins, inferior vena cava, duodenum, small bowel, colon, left/right kidney, urinary bladder), and machine learning classifiers were trained with 5-fold cross-validation to classify IV contrast phases (noncontrast, pulmonary arterial, arterial, venous, and urographic) and GIT contrast enhancement. The performance of the ensembles was evaluated using the receiver operating characteristic area under the curve (AUC) and 95% confidence intervals (CIs). RESULTS For the IV phase classification task, the following AUC scores were obtained for the internal test set: 99.59% [95% CI, 99.58-99.63] for the noncontrast phase, 99.50% [95% CI, 99.49-99.52] for the pulmonary-arterial phase, 99.13% [95% CI, 99.10-99.15] for the arterial phase, 99.8% [95% CI, 99.79-99.81] for the venous phase, and 99.7% [95% CI, 99.68-99.7] for the urographic phase. For the external dataset, a mean AUC of 97.33% [95% CI, 97.27-97.35] and 97.38% [95% CI, 97.34-97.41] was achieved for all contrast phases for the first and second annotators, respectively. Contrast media in the GIT could be identified with an AUC of 99.90% [95% CI, 99.89-99.9] in the internal dataset, whereas in the external dataset, an AUC of 99.73% [95% CI, 99.71-99.73] and 99.31% [95% CI, 99.27-99.33] was achieved with the first and second annotator, respectively. CONCLUSIONS The integration of open-source segmentation networks and classifiers effectively classified contrast phases and identified GIT contrast enhancement using anatomical landmarks.
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Affiliation(s)
- Giulia Baldini
- From the Institute of Interventional and Diagnostic Radiology and Neuroradiology, University Hospital Essen, Essen, Germany (G.B., R.H., K.B., L.K., S.K., F.N., J.H.); Institute for Artificial Intelligence in Medicine, University Hospital Essen, Essen, Germany (G.B., R.H., C.S.S., K.B., L.K., S.K., O.P., F.N., J.H.); Institute for Transfusion Medicine, University Hospital Essen, Essen, Germany (C.S.S.); Department of Diagnostic and Interventional Radiology, Kliniken Essen-Mitte, Essen, Germany (P.H.); and Data Integration Center, Central IT Department, University Hospital Essen, Essen, Germany (O.P., F.N.)
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Drews MA, Milosevic A, Hamacher R, Grüneisen JS, Haubold J, Opitz MK, Bauer S, Umutlu L, Forsting M, Schaarschmidt BM. Impact of CT and MRI in the diagnostic workup of malignant triton tumour-a monocentric analysis and review of the literature. Br J Radiol 2024; 97:430-438. [PMID: 38308031 DOI: 10.1093/bjr/tqad035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Revised: 11/15/2023] [Accepted: 11/20/2023] [Indexed: 02/04/2024] Open
Abstract
OBJECTIVES Malignant triton tumours (MTTs) are rare but aggressive subtypes of malignant peripheral nerve sheath tumours (MPNSTs) with a high recurrence rate and 5-year survival of 14%. Systematic imaging data on MTTs are scarce and mainly based on single case reports. Therefore, we aimed to identify typical CT and MRI features to improve early diagnosis rates of this uncommon entity. METHODS A systematic review on literature published until December 2022 on imaging characteristics of MTTs was performed. Based on that, we conducted a retrospective, monocentric analysis of patients with histopathologically proven MTTs from our department. Explorative data analysis was performed. RESULTS Initially, 29 studies on 34 patients (31.42 ± 22.6 years, 12 female) were evaluated: Literature described primary MTTs as huge, lobulated tumours (108 ± 99.3 mm) with central necrosis (56% [19/34]), low T1w (81% [17/21]), high T2w signal (90% [19/21]) and inhomogeneous enhancement on MRI (54% [7/13]). Analysis of 16 patients (48.9 ± 13.8 years; 9 female) from our institution revealed comparable results: primary MTTs showed large, lobulated masses (118 mm ± 64.9) with necrotic areas (92% [11/12]). MRI revealed low T1w (100% [7/7]), high T2w signal (100% [7/7]) and inhomogeneous enhancement (86% [6/7]). Local recurrences and soft-tissue metastases mimicked these features, while nonsoft-tissue metastases appeared unspecific. CONCLUSIONS MTTs show characteristic features on CT and MRI. However, these do not allow a reliable differentiation between MTTs and other MPNSTs based on imaging alone. Therefore, additional histopathological analysis is required. ADVANCES IN KNOWLEDGE This largest published systematic analysis on MTT imaging revealed typical but unspecific imaging features that do not allow a reliable, imaging-based differentiation between MTTs and other MPNSTs. Hence, additional histopathological analysis remains essential.
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Affiliation(s)
- Marcel A Drews
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, 45147 Essen, Germany
| | - Aleksandar Milosevic
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, 45147 Essen, Germany
| | - Rainer Hamacher
- West German Cancer Centre, Department of Medical Oncology, University Hospital Essen, 45147 Essen, Germany
| | - Johannes S Grüneisen
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, 45147 Essen, Germany
| | - Johannes Haubold
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, 45147 Essen, Germany
| | - Marcel K Opitz
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, 45147 Essen, Germany
| | - Sebastian Bauer
- West German Cancer Centre, Department of Medical Oncology, University Hospital Essen, 45147 Essen, Germany
| | - Lale Umutlu
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, 45147 Essen, Germany
| | - Michael Forsting
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, 45147 Essen, Germany
| | - Benedikt M Schaarschmidt
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, 45147 Essen, Germany
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Auer TA, Müller L, Schulze D, Anhamm M, Bettinger D, Steinle V, Haubold J, Zopfs D, Pinto Dos Santos D, Eisenblätter M, Gebauer B, Kloeckner R, Collettini F. CT-guided High-Dose-Rate Brachytherapy versus Transarterial Chemoembolization in Patients with Unresectable Hepatocellular Carcinoma. Radiology 2024; 310:e232044. [PMID: 38319166 DOI: 10.1148/radiol.232044] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2024]
Abstract
Background CT-guided high-dose-rate (HDR) brachytherapy (hereafter, HDR brachytherapy) has been shown to be safe and effective for patients with unresectable hepatocellular carcinoma (HCC), but studies comparing this therapy with other local-regional therapies are scarce. Purpose To compare patient outcomes of HDR brachytherapy and transarterial chemoembolization (TACE) in patients with unresectable HCC. Materials and Methods This multi-institutional retrospective study included consecutive treatment-naive adult patients with unresectable HCC who underwent either HDR brachytherapy or TACE between January 2010 and December 2022. Overall survival (OS) and progression-free survival (PFS) were compared between patients matched for clinical and tumor characteristics by propensity score matching. Not all patients who underwent TACE had PFS available; thus, a different set of patients was used for PFS and OS analysis for this treatment. Hazard ratios (HRs) were calculated from Kaplan-Meier survival curves. Results After propensity matching, 150 patients who underwent HDR brachytherapy (median age, 71 years [IQR, 63-77 years]; 117 males) and 150 patients who underwent TACE (OS analysis median age, 70 years [IQR, 63-77 years]; 119 male; PFS analysis median age, 68 years [IQR: 63-76 years]; 119 male) were analyzed. Hazard of death was higher in the TACE versus HDR brachytherapy group (HR, 4.04; P < .001). Median estimated PFS was 32.8 months (95% CI: 12.5, 58.7) in the HDR brachytherapy group and 11.6 months (95% CI: 4.9, 22.7) in the TACE group. Hazard of disease progression was higher in the TACE versus HDR brachytherapy group (HR, 2.23; P < .001). Conclusion In selected treatment-naive patients with unresectable HCC, treatment with CT-guided HDR brachytherapy led to improved OS and PFS compared with TACE. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Chapiro in this issue.
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Affiliation(s)
- Timo A Auer
- From the Department of Radiology, Charité-Universitätsmedizin Berlin, Charité Campus Mitte, Charitéplatz 1, 10117 Berlin, Germany (T.A.A., M.A., B.G., F.C.); Berlin Institute of Health, Berlin, Germany (T.A.A., F.C.); Department of Diagnostic and Interventional Radiology, University Medical Center, Mainz, Mainz, Germany (L.M.); Institute of Biometry and Clinical Epidemiology, Charité Universitätsmedizin Berlin, Berlin, Germany (D.S.); Department of Medicine II, University of Freiburg Medical Center, Freiburg, Germany (D.B.); Department of Diagnostic and Interventional Radiology, University Medical Center Heidelberg, Heidelberg, Germany (V.S.); Institute of Diagnostic and Interventional Radiology and Institute for Artificial Intelligence in Medicine, University Hospital Essen, Essen, Germany (J.H.); Institute for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany (D.Z., D.P.d.S.); Institute of Diagnostic and Interventional Radiology, University Hospital of Frankfurt, Frankfurt, Germany (D.P.d.S.); Department of Diagnostic and Interventional Radiology, Medical Faculty OWL, Bielefeld University, Bielefeld, Germany (M.E.); and Institute of Interventional Radiology, University Hospital Schleswig-Holstein-Campus Lübeck, Lübeck, Germany (R.K.)
| | - Lukas Müller
- From the Department of Radiology, Charité-Universitätsmedizin Berlin, Charité Campus Mitte, Charitéplatz 1, 10117 Berlin, Germany (T.A.A., M.A., B.G., F.C.); Berlin Institute of Health, Berlin, Germany (T.A.A., F.C.); Department of Diagnostic and Interventional Radiology, University Medical Center, Mainz, Mainz, Germany (L.M.); Institute of Biometry and Clinical Epidemiology, Charité Universitätsmedizin Berlin, Berlin, Germany (D.S.); Department of Medicine II, University of Freiburg Medical Center, Freiburg, Germany (D.B.); Department of Diagnostic and Interventional Radiology, University Medical Center Heidelberg, Heidelberg, Germany (V.S.); Institute of Diagnostic and Interventional Radiology and Institute for Artificial Intelligence in Medicine, University Hospital Essen, Essen, Germany (J.H.); Institute for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany (D.Z., D.P.d.S.); Institute of Diagnostic and Interventional Radiology, University Hospital of Frankfurt, Frankfurt, Germany (D.P.d.S.); Department of Diagnostic and Interventional Radiology, Medical Faculty OWL, Bielefeld University, Bielefeld, Germany (M.E.); and Institute of Interventional Radiology, University Hospital Schleswig-Holstein-Campus Lübeck, Lübeck, Germany (R.K.)
| | - Daniel Schulze
- From the Department of Radiology, Charité-Universitätsmedizin Berlin, Charité Campus Mitte, Charitéplatz 1, 10117 Berlin, Germany (T.A.A., M.A., B.G., F.C.); Berlin Institute of Health, Berlin, Germany (T.A.A., F.C.); Department of Diagnostic and Interventional Radiology, University Medical Center, Mainz, Mainz, Germany (L.M.); Institute of Biometry and Clinical Epidemiology, Charité Universitätsmedizin Berlin, Berlin, Germany (D.S.); Department of Medicine II, University of Freiburg Medical Center, Freiburg, Germany (D.B.); Department of Diagnostic and Interventional Radiology, University Medical Center Heidelberg, Heidelberg, Germany (V.S.); Institute of Diagnostic and Interventional Radiology and Institute for Artificial Intelligence in Medicine, University Hospital Essen, Essen, Germany (J.H.); Institute for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany (D.Z., D.P.d.S.); Institute of Diagnostic and Interventional Radiology, University Hospital of Frankfurt, Frankfurt, Germany (D.P.d.S.); Department of Diagnostic and Interventional Radiology, Medical Faculty OWL, Bielefeld University, Bielefeld, Germany (M.E.); and Institute of Interventional Radiology, University Hospital Schleswig-Holstein-Campus Lübeck, Lübeck, Germany (R.K.)
| | - Melina Anhamm
- From the Department of Radiology, Charité-Universitätsmedizin Berlin, Charité Campus Mitte, Charitéplatz 1, 10117 Berlin, Germany (T.A.A., M.A., B.G., F.C.); Berlin Institute of Health, Berlin, Germany (T.A.A., F.C.); Department of Diagnostic and Interventional Radiology, University Medical Center, Mainz, Mainz, Germany (L.M.); Institute of Biometry and Clinical Epidemiology, Charité Universitätsmedizin Berlin, Berlin, Germany (D.S.); Department of Medicine II, University of Freiburg Medical Center, Freiburg, Germany (D.B.); Department of Diagnostic and Interventional Radiology, University Medical Center Heidelberg, Heidelberg, Germany (V.S.); Institute of Diagnostic and Interventional Radiology and Institute for Artificial Intelligence in Medicine, University Hospital Essen, Essen, Germany (J.H.); Institute for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany (D.Z., D.P.d.S.); Institute of Diagnostic and Interventional Radiology, University Hospital of Frankfurt, Frankfurt, Germany (D.P.d.S.); Department of Diagnostic and Interventional Radiology, Medical Faculty OWL, Bielefeld University, Bielefeld, Germany (M.E.); and Institute of Interventional Radiology, University Hospital Schleswig-Holstein-Campus Lübeck, Lübeck, Germany (R.K.)
| | - Dominik Bettinger
- From the Department of Radiology, Charité-Universitätsmedizin Berlin, Charité Campus Mitte, Charitéplatz 1, 10117 Berlin, Germany (T.A.A., M.A., B.G., F.C.); Berlin Institute of Health, Berlin, Germany (T.A.A., F.C.); Department of Diagnostic and Interventional Radiology, University Medical Center, Mainz, Mainz, Germany (L.M.); Institute of Biometry and Clinical Epidemiology, Charité Universitätsmedizin Berlin, Berlin, Germany (D.S.); Department of Medicine II, University of Freiburg Medical Center, Freiburg, Germany (D.B.); Department of Diagnostic and Interventional Radiology, University Medical Center Heidelberg, Heidelberg, Germany (V.S.); Institute of Diagnostic and Interventional Radiology and Institute for Artificial Intelligence in Medicine, University Hospital Essen, Essen, Germany (J.H.); Institute for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany (D.Z., D.P.d.S.); Institute of Diagnostic and Interventional Radiology, University Hospital of Frankfurt, Frankfurt, Germany (D.P.d.S.); Department of Diagnostic and Interventional Radiology, Medical Faculty OWL, Bielefeld University, Bielefeld, Germany (M.E.); and Institute of Interventional Radiology, University Hospital Schleswig-Holstein-Campus Lübeck, Lübeck, Germany (R.K.)
| | - Verena Steinle
- From the Department of Radiology, Charité-Universitätsmedizin Berlin, Charité Campus Mitte, Charitéplatz 1, 10117 Berlin, Germany (T.A.A., M.A., B.G., F.C.); Berlin Institute of Health, Berlin, Germany (T.A.A., F.C.); Department of Diagnostic and Interventional Radiology, University Medical Center, Mainz, Mainz, Germany (L.M.); Institute of Biometry and Clinical Epidemiology, Charité Universitätsmedizin Berlin, Berlin, Germany (D.S.); Department of Medicine II, University of Freiburg Medical Center, Freiburg, Germany (D.B.); Department of Diagnostic and Interventional Radiology, University Medical Center Heidelberg, Heidelberg, Germany (V.S.); Institute of Diagnostic and Interventional Radiology and Institute for Artificial Intelligence in Medicine, University Hospital Essen, Essen, Germany (J.H.); Institute for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany (D.Z., D.P.d.S.); Institute of Diagnostic and Interventional Radiology, University Hospital of Frankfurt, Frankfurt, Germany (D.P.d.S.); Department of Diagnostic and Interventional Radiology, Medical Faculty OWL, Bielefeld University, Bielefeld, Germany (M.E.); and Institute of Interventional Radiology, University Hospital Schleswig-Holstein-Campus Lübeck, Lübeck, Germany (R.K.)
| | - Johannes Haubold
- From the Department of Radiology, Charité-Universitätsmedizin Berlin, Charité Campus Mitte, Charitéplatz 1, 10117 Berlin, Germany (T.A.A., M.A., B.G., F.C.); Berlin Institute of Health, Berlin, Germany (T.A.A., F.C.); Department of Diagnostic and Interventional Radiology, University Medical Center, Mainz, Mainz, Germany (L.M.); Institute of Biometry and Clinical Epidemiology, Charité Universitätsmedizin Berlin, Berlin, Germany (D.S.); Department of Medicine II, University of Freiburg Medical Center, Freiburg, Germany (D.B.); Department of Diagnostic and Interventional Radiology, University Medical Center Heidelberg, Heidelberg, Germany (V.S.); Institute of Diagnostic and Interventional Radiology and Institute for Artificial Intelligence in Medicine, University Hospital Essen, Essen, Germany (J.H.); Institute for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany (D.Z., D.P.d.S.); Institute of Diagnostic and Interventional Radiology, University Hospital of Frankfurt, Frankfurt, Germany (D.P.d.S.); Department of Diagnostic and Interventional Radiology, Medical Faculty OWL, Bielefeld University, Bielefeld, Germany (M.E.); and Institute of Interventional Radiology, University Hospital Schleswig-Holstein-Campus Lübeck, Lübeck, Germany (R.K.)
| | - David Zopfs
- From the Department of Radiology, Charité-Universitätsmedizin Berlin, Charité Campus Mitte, Charitéplatz 1, 10117 Berlin, Germany (T.A.A., M.A., B.G., F.C.); Berlin Institute of Health, Berlin, Germany (T.A.A., F.C.); Department of Diagnostic and Interventional Radiology, University Medical Center, Mainz, Mainz, Germany (L.M.); Institute of Biometry and Clinical Epidemiology, Charité Universitätsmedizin Berlin, Berlin, Germany (D.S.); Department of Medicine II, University of Freiburg Medical Center, Freiburg, Germany (D.B.); Department of Diagnostic and Interventional Radiology, University Medical Center Heidelberg, Heidelberg, Germany (V.S.); Institute of Diagnostic and Interventional Radiology and Institute for Artificial Intelligence in Medicine, University Hospital Essen, Essen, Germany (J.H.); Institute for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany (D.Z., D.P.d.S.); Institute of Diagnostic and Interventional Radiology, University Hospital of Frankfurt, Frankfurt, Germany (D.P.d.S.); Department of Diagnostic and Interventional Radiology, Medical Faculty OWL, Bielefeld University, Bielefeld, Germany (M.E.); and Institute of Interventional Radiology, University Hospital Schleswig-Holstein-Campus Lübeck, Lübeck, Germany (R.K.)
| | - Daniel Pinto Dos Santos
- From the Department of Radiology, Charité-Universitätsmedizin Berlin, Charité Campus Mitte, Charitéplatz 1, 10117 Berlin, Germany (T.A.A., M.A., B.G., F.C.); Berlin Institute of Health, Berlin, Germany (T.A.A., F.C.); Department of Diagnostic and Interventional Radiology, University Medical Center, Mainz, Mainz, Germany (L.M.); Institute of Biometry and Clinical Epidemiology, Charité Universitätsmedizin Berlin, Berlin, Germany (D.S.); Department of Medicine II, University of Freiburg Medical Center, Freiburg, Germany (D.B.); Department of Diagnostic and Interventional Radiology, University Medical Center Heidelberg, Heidelberg, Germany (V.S.); Institute of Diagnostic and Interventional Radiology and Institute for Artificial Intelligence in Medicine, University Hospital Essen, Essen, Germany (J.H.); Institute for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany (D.Z., D.P.d.S.); Institute of Diagnostic and Interventional Radiology, University Hospital of Frankfurt, Frankfurt, Germany (D.P.d.S.); Department of Diagnostic and Interventional Radiology, Medical Faculty OWL, Bielefeld University, Bielefeld, Germany (M.E.); and Institute of Interventional Radiology, University Hospital Schleswig-Holstein-Campus Lübeck, Lübeck, Germany (R.K.)
| | - Michel Eisenblätter
- From the Department of Radiology, Charité-Universitätsmedizin Berlin, Charité Campus Mitte, Charitéplatz 1, 10117 Berlin, Germany (T.A.A., M.A., B.G., F.C.); Berlin Institute of Health, Berlin, Germany (T.A.A., F.C.); Department of Diagnostic and Interventional Radiology, University Medical Center, Mainz, Mainz, Germany (L.M.); Institute of Biometry and Clinical Epidemiology, Charité Universitätsmedizin Berlin, Berlin, Germany (D.S.); Department of Medicine II, University of Freiburg Medical Center, Freiburg, Germany (D.B.); Department of Diagnostic and Interventional Radiology, University Medical Center Heidelberg, Heidelberg, Germany (V.S.); Institute of Diagnostic and Interventional Radiology and Institute for Artificial Intelligence in Medicine, University Hospital Essen, Essen, Germany (J.H.); Institute for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany (D.Z., D.P.d.S.); Institute of Diagnostic and Interventional Radiology, University Hospital of Frankfurt, Frankfurt, Germany (D.P.d.S.); Department of Diagnostic and Interventional Radiology, Medical Faculty OWL, Bielefeld University, Bielefeld, Germany (M.E.); and Institute of Interventional Radiology, University Hospital Schleswig-Holstein-Campus Lübeck, Lübeck, Germany (R.K.)
| | - Bernhard Gebauer
- From the Department of Radiology, Charité-Universitätsmedizin Berlin, Charité Campus Mitte, Charitéplatz 1, 10117 Berlin, Germany (T.A.A., M.A., B.G., F.C.); Berlin Institute of Health, Berlin, Germany (T.A.A., F.C.); Department of Diagnostic and Interventional Radiology, University Medical Center, Mainz, Mainz, Germany (L.M.); Institute of Biometry and Clinical Epidemiology, Charité Universitätsmedizin Berlin, Berlin, Germany (D.S.); Department of Medicine II, University of Freiburg Medical Center, Freiburg, Germany (D.B.); Department of Diagnostic and Interventional Radiology, University Medical Center Heidelberg, Heidelberg, Germany (V.S.); Institute of Diagnostic and Interventional Radiology and Institute for Artificial Intelligence in Medicine, University Hospital Essen, Essen, Germany (J.H.); Institute for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany (D.Z., D.P.d.S.); Institute of Diagnostic and Interventional Radiology, University Hospital of Frankfurt, Frankfurt, Germany (D.P.d.S.); Department of Diagnostic and Interventional Radiology, Medical Faculty OWL, Bielefeld University, Bielefeld, Germany (M.E.); and Institute of Interventional Radiology, University Hospital Schleswig-Holstein-Campus Lübeck, Lübeck, Germany (R.K.)
| | - Roman Kloeckner
- From the Department of Radiology, Charité-Universitätsmedizin Berlin, Charité Campus Mitte, Charitéplatz 1, 10117 Berlin, Germany (T.A.A., M.A., B.G., F.C.); Berlin Institute of Health, Berlin, Germany (T.A.A., F.C.); Department of Diagnostic and Interventional Radiology, University Medical Center, Mainz, Mainz, Germany (L.M.); Institute of Biometry and Clinical Epidemiology, Charité Universitätsmedizin Berlin, Berlin, Germany (D.S.); Department of Medicine II, University of Freiburg Medical Center, Freiburg, Germany (D.B.); Department of Diagnostic and Interventional Radiology, University Medical Center Heidelberg, Heidelberg, Germany (V.S.); Institute of Diagnostic and Interventional Radiology and Institute for Artificial Intelligence in Medicine, University Hospital Essen, Essen, Germany (J.H.); Institute for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany (D.Z., D.P.d.S.); Institute of Diagnostic and Interventional Radiology, University Hospital of Frankfurt, Frankfurt, Germany (D.P.d.S.); Department of Diagnostic and Interventional Radiology, Medical Faculty OWL, Bielefeld University, Bielefeld, Germany (M.E.); and Institute of Interventional Radiology, University Hospital Schleswig-Holstein-Campus Lübeck, Lübeck, Germany (R.K.)
| | - Federico Collettini
- From the Department of Radiology, Charité-Universitätsmedizin Berlin, Charité Campus Mitte, Charitéplatz 1, 10117 Berlin, Germany (T.A.A., M.A., B.G., F.C.); Berlin Institute of Health, Berlin, Germany (T.A.A., F.C.); Department of Diagnostic and Interventional Radiology, University Medical Center, Mainz, Mainz, Germany (L.M.); Institute of Biometry and Clinical Epidemiology, Charité Universitätsmedizin Berlin, Berlin, Germany (D.S.); Department of Medicine II, University of Freiburg Medical Center, Freiburg, Germany (D.B.); Department of Diagnostic and Interventional Radiology, University Medical Center Heidelberg, Heidelberg, Germany (V.S.); Institute of Diagnostic and Interventional Radiology and Institute for Artificial Intelligence in Medicine, University Hospital Essen, Essen, Germany (J.H.); Institute for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany (D.Z., D.P.d.S.); Institute of Diagnostic and Interventional Radiology, University Hospital of Frankfurt, Frankfurt, Germany (D.P.d.S.); Department of Diagnostic and Interventional Radiology, Medical Faculty OWL, Bielefeld University, Bielefeld, Germany (M.E.); and Institute of Interventional Radiology, University Hospital Schleswig-Holstein-Campus Lübeck, Lübeck, Germany (R.K.)
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Haubold J, Hosch R, Jost G, Kreis F, Forsting M, Pietsch H, Nensa F. AI as a New Frontier in Contrast Media Research: Bridging the Gap Between Contrast Media Reduction, the Contrast-Free Question and New Application Discoveries. Invest Radiol 2024; 59:206-213. [PMID: 37824140 DOI: 10.1097/rli.0000000000001028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/13/2023]
Abstract
ABSTRACT Artificial intelligence (AI) techniques are currently harnessed to revolutionize the domain of medical imaging. This review investigates 3 major AI-driven approaches for contrast agent management: new frontiers in contrast agent dose reduction, the contrast-free question, and new applications. By examining recent studies that use AI as a new frontier in contrast media research, we synthesize the current state of the field and provide a comprehensive understanding of the potential and limitations of AI in this context. In doing so, we show the dose limits of reducing the amount of contrast agents and demonstrate why it might not be possible to completely eliminate contrast agents in the future. In addition, we highlight potential new applications to further increase the radiologist's sensitivity at normal doses. At the same time, this review shows which network architectures provide promising approaches and reveals possible artifacts of a paired image-to-image conversion. Furthermore, current US Food and Drug Administration regulatory guidelines regarding AI/machine learning-enabled medical devices are highlighted.
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Affiliation(s)
- Johannes Haubold
- From the Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany (J.H., R.H., M.F., F.N.); Institute of Artificial Intelligence in Medicine, University Hospital Essen, Essen, Germany (J.H., R.H., F.N.); and MR and CT Contrast Media Research, Bayer AG, Berlin, Germany (G.J., F.K., H.P.)
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15
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Salhöfer L, Haubold J, Gutt M, Hosch R, Umutlu L, Meetschen M, Schuessler M, Forsting M, Nensa F, Schaarschmidt BM. The importance of educational tools and a new software solution for visualizing and quantifying report correction in radiology training. Sci Rep 2024; 14:1172. [PMID: 38216664 PMCID: PMC10786897 DOI: 10.1038/s41598-024-51462-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2023] [Accepted: 01/05/2024] [Indexed: 01/14/2024] Open
Abstract
A novel software, DiffTool, was developed in-house to keep track of changes made by board-certified radiologists to preliminary reports created by residents and evaluate its impact on radiological hands-on training. Before (t0) and after (t2-4) the deployment of the software, 18 residents (median age: 29 years; 33% female) completed a standardized questionnaire on professional training. At t2-4 the participants were also requested to respond to three additional questions to evaluate the software. Responses were recorded via a six-point Likert scale ranging from 1 ("strongly agree") to 6 ("strongly disagree"). Prior to the release of the software, 39% (7/18) of the residents strongly agreed with the statement that they manually tracked changes made by board-certified radiologists to each of their radiological reports while 61% were less inclined to agree with that statement. At t2-4, 61% (11/18) stated that they used DiffTool to track differences. Furthermore, we observed an increase from 33% (6/18) to 44% (8/18) of residents who agreed to the statement "I profit from every corrected report". The DiffTool was well accepted among residents with a regular user base of 72% (13/18), while 78% (14/18) considered it a relevant improvement to their training. The results of this study demonstrate the importance of providing a time-efficient way to analyze changes made to preliminary reports as an additive for professional training.
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Affiliation(s)
- Luca Salhöfer
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Hufelandstr. 55, 45147, Essen, Germany.
- Institute for Artificial Intelligence in Medicine, University Hospital Essen, Essen, Germany.
| | - Johannes Haubold
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Hufelandstr. 55, 45147, Essen, Germany
- Institute for Artificial Intelligence in Medicine, University Hospital Essen, Essen, Germany
| | - Maurice Gutt
- Central IT Services, University Hospital Essen, Essen, Germany
| | - René Hosch
- Institute for Artificial Intelligence in Medicine, University Hospital Essen, Essen, Germany
| | - Lale Umutlu
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Hufelandstr. 55, 45147, Essen, Germany
| | - Mathias Meetschen
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Hufelandstr. 55, 45147, Essen, Germany
- Institute for Artificial Intelligence in Medicine, University Hospital Essen, Essen, Germany
| | - Maximilian Schuessler
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Hufelandstr. 55, 45147, Essen, Germany
- Institute for Artificial Intelligence in Medicine, University Hospital Essen, Essen, Germany
| | - Michael Forsting
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Hufelandstr. 55, 45147, Essen, Germany
| | - Felix Nensa
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Hufelandstr. 55, 45147, Essen, Germany
- Institute for Artificial Intelligence in Medicine, University Hospital Essen, Essen, Germany
| | - Benedikt Michael Schaarschmidt
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Hufelandstr. 55, 45147, Essen, Germany
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16
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Rempe M, Mentzel F, Pomykala KL, Haubold J, Nensa F, Kroeninger K, Egger J, Kleesiek J. k-strip: A novel segmentation algorithm in k-space for the application of skull stripping. Comput Methods Programs Biomed 2024; 243:107912. [PMID: 37981454 DOI: 10.1016/j.cmpb.2023.107912] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Revised: 10/30/2023] [Accepted: 11/02/2023] [Indexed: 11/21/2023]
Abstract
BACKGROUND AND OBJECTIVE We present a novel deep learning-based skull stripping algorithm for magnetic resonance imaging (MRI) that works directly in the information rich complex valued k-space. METHODS Using four datasets from different institutions with a total of around 200,000 MRI slices, we show that our network can perform skull-stripping on the raw data of MRIs while preserving the phase information which no other skull stripping algorithm is able to work with. For two of the datasets, skull stripping performed by HD-BET (Brain Extraction Tool) in the image domain is used as the ground truth, whereas the third and fourth dataset comes with per-hand annotated brain segmentations. RESULTS All four datasets were very similar to the ground truth (DICE scores of 92 %-99 % and Hausdorff distances of under 5.5 pixel). Results on slices above the eye-region reach DICE scores of up to 99 %, whereas the accuracy drops in regions around the eyes and below, with partially blurred output. The output of k-Strip often has smoothed edges at the demarcation to the skull. Binary masks are created with an appropriate threshold. CONCLUSION With this proof-of-concept study, we were able to show the feasibility of working in the k-space frequency domain, preserving phase information, with consistent results. Besides preserving valuable information for further diagnostics, this approach makes an immediate anonymization of patient data possible, already before being transformed into the image domain. Future research should be dedicated to discovering additional ways the k-space can be used for innovative image analysis and further workflows.
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Affiliation(s)
- Moritz Rempe
- The Institute for AI in Medicine (IKIM), University Hospital Essen, Girardetstraße 2, Essen 45131, Germany; Otto-Hahn-Straße 4a, Department of Physics of the Technical University Dortmund, Dortmund 44227, Germany
| | - Florian Mentzel
- Otto-Hahn-Straße 4a, Department of Physics of the Technical University Dortmund, Dortmund 44227, Germany
| | - Kelsey L Pomykala
- The Institute for AI in Medicine (IKIM), University Hospital Essen, Girardetstraße 2, Essen 45131, Germany
| | - Johannes Haubold
- The Institute for AI in Medicine (IKIM), University Hospital Essen, Girardetstraße 2, Essen 45131, Germany
| | - Felix Nensa
- The Institute for AI in Medicine (IKIM), University Hospital Essen, Girardetstraße 2, Essen 45131, Germany
| | - Kevin Kroeninger
- Otto-Hahn-Straße 4a, Department of Physics of the Technical University Dortmund, Dortmund 44227, Germany
| | - Jan Egger
- The Institute for AI in Medicine (IKIM), University Hospital Essen, Girardetstraße 2, Essen 45131, Germany; The Computer Algorithms for Medicine Laboratory, Graz, Austria; The Institute of Computer Graphics and Vision, Inffeldgasse 16, Graz University of Technology, Graz 8010, Austria; Cancer Research Center Cologne Essen (CCCE), Hufelandstraße 55, University Medicine Essen, Essen 45147, Germany
| | - Jens Kleesiek
- The Institute for AI in Medicine (IKIM), University Hospital Essen, Girardetstraße 2, Essen 45131, Germany; Cancer Research Center Cologne Essen (CCCE), Hufelandstraße 55, University Medicine Essen, Essen 45147, Germany; Partner Site Essen, Hufelandstraße 55, German Cancer Consortium (DKTK), Essen 45147, Germany.
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17
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Keyl J, Bucher A, Jungmann F, Hosch R, Ziller A, Armbruster R, Malkomes P, Reissig TM, Koitka S, Tzianopoulos I, Keyl P, Kostbade K, Albers D, Markus P, Treckmann J, Nassenstein K, Haubold J, Makowski M, Forsting M, Baba HA, Kasper S, Siveke JT, Nensa F, Schuler M, Kaissis G, Kleesiek J, Braren R. Prognostic value of deep learning-derived body composition in advanced pancreatic cancer-a retrospective multicenter study. ESMO Open 2024; 9:102219. [PMID: 38194881 PMCID: PMC10837775 DOI: 10.1016/j.esmoop.2023.102219] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2023] [Revised: 12/11/2023] [Accepted: 12/13/2023] [Indexed: 01/11/2024] Open
Abstract
BACKGROUND Despite the prognostic relevance of cachexia in pancreatic cancer, individual body composition has not been routinely integrated into treatment planning. In this multicenter study, we investigated the prognostic value of sarcopenia and myosteatosis automatically extracted from routine computed tomography (CT) scans of patients with advanced pancreatic ductal adenocarcinoma (PDAC). PATIENTS AND METHODS We retrospectively analyzed clinical imaging data of 601 patients from three German cancer centers. We applied a deep learning approach to assess sarcopenia by the abdominal muscle-to-bone ratio (MBR) and myosteatosis by the ratio of abdominal inter- and intramuscular fat to muscle volume. In the pooled cohort, univariable and multivariable analyses were carried out to analyze the association between body composition markers and overall survival (OS). We analyzed the relationship between body composition markers and laboratory values during the first year of therapy in a subgroup using linear regression analysis adjusted for age, sex, and American Joint Committee on Cancer (AJCC) stage. RESULTS Deep learning-derived MBR [hazard ratio (HR) 0.60, 95% confidence interval (CI) 0.47-0.77, P < 0.005] and myosteatosis (HR 3.73, 95% CI 1.66-8.39, P < 0.005) were significantly associated with OS in univariable analysis. In multivariable analysis, MBR (P = 0.019) and myosteatosis (P = 0.02) were associated with OS independent of age, sex, and AJCC stage. In a subgroup, MBR and myosteatosis were associated with albumin and C-reactive protein levels after initiation of therapy. Additionally, MBR was also associated with hemoglobin and total protein levels. CONCLUSIONS Our work demonstrates that deep learning can be applied across cancer centers to automatically assess sarcopenia and myosteatosis from routine CT scans. We highlight the prognostic role of our proposed markers and show a strong relationship with protein levels, inflammation, and anemia. In clinical practice, automated body composition analysis holds the potential to further personalize cancer treatment.
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Affiliation(s)
- J Keyl
- Institute for Artificial Intelligence in Medicine, University Hospital Essen (AöR), Essen, Germany; Institute of Pathology, University Hospital Essen (AöR), Essen, Germany.
| | - A Bucher
- Institute for Diagnostic and Interventional Radiology, Goethe University Frankfurt, Frankfurt am Main, Germany; German Cancer Consortium (DKTK), Frankfurt partner site, Heidelberg, Germany
| | - F Jungmann
- Institute of Diagnostic and Interventional Radiology, Technical University of Munich, School of Medicine, Munich, Germany; Artificial Intelligence in Healthcare and Medicine, School of Computation, Information and Technology, Technical University of Munich, Munich, Germany
| | - R Hosch
- Institute for Artificial Intelligence in Medicine, University Hospital Essen (AöR), Essen, Germany
| | - A Ziller
- Institute of Diagnostic and Interventional Radiology, Technical University of Munich, School of Medicine, Munich, Germany; Artificial Intelligence in Healthcare and Medicine, School of Computation, Information and Technology, Technical University of Munich, Munich, Germany
| | - R Armbruster
- Institute for Diagnostic and Interventional Radiology, Goethe University Frankfurt, Frankfurt am Main, Germany
| | - P Malkomes
- Department of General, Visceral and Transplant Surgery, Goethe University Hospital Frankfurt, Frankfurt am Main, Germany
| | - T M Reissig
- Department of Medical Oncology, University Hospital Essen (AöR), Essen, Germany; West German Cancer Center, University Hospital Essen (AöR), Essen, Germany; German Cancer Consortium (DKTK), Partner site University Hospital Essen (AöR), Essen, Germany; Bridge Institute of Experimental Tumor Therapy, West German Cancer Center, University Hospital Essen, University of Duisburg-Essen, Essen, Germany; Division of Solid Tumor Translational Oncology, German Cancer Consortium (DKTK Partner Site Essen) and German Cancer Research Center, DKFZ, Heidelberg, Germany
| | - S Koitka
- Institute for Artificial Intelligence in Medicine, University Hospital Essen (AöR), Essen, Germany; Institute for Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen (AöR), Essen, Germany
| | - I Tzianopoulos
- Department of Medical Oncology, University Hospital Essen (AöR), Essen, Germany; West German Cancer Center, University Hospital Essen (AöR), Essen, Germany; Bridge Institute of Experimental Tumor Therapy, West German Cancer Center, University Hospital Essen, University of Duisburg-Essen, Essen, Germany; Division of Solid Tumor Translational Oncology, German Cancer Consortium (DKTK Partner Site Essen) and German Cancer Research Center, DKFZ, Heidelberg, Germany
| | - P Keyl
- Institute of Pathology, Ludwig-Maximilians-University Munich, Munich, Germany
| | - K Kostbade
- Department of Medical Oncology, University Hospital Essen (AöR), Essen, Germany; West German Cancer Center, University Hospital Essen (AöR), Essen, Germany; Medical Faculty, University of Duisburg-Essen, Essen, Germany
| | - D Albers
- Department of Gastroenterology, Elisabeth Hospital Essen, Essen, Germany
| | - P Markus
- Department of General Surgery and Traumatology, Elisabeth Hospital Essen, Essen, Germany
| | - J Treckmann
- West German Cancer Center, University Hospital Essen (AöR), Essen, Germany; Medical Faculty, University of Duisburg-Essen, Essen, Germany; Department of General, Visceral and Transplant Surgery, University Hospital Essen, Essen, Germany
| | - K Nassenstein
- Institute for Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen (AöR), Essen, Germany; Medical Faculty, University of Duisburg-Essen, Essen, Germany
| | - J Haubold
- Institute for Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen (AöR), Essen, Germany; Medical Faculty, University of Duisburg-Essen, Essen, Germany
| | - M Makowski
- Institute of Diagnostic and Interventional Radiology, Technical University of Munich, School of Medicine, Munich, Germany
| | - M Forsting
- German Cancer Consortium (DKTK), Partner site University Hospital Essen (AöR), Essen, Germany; Institute for Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen (AöR), Essen, Germany; Medical Faculty, University of Duisburg-Essen, Essen, Germany
| | - H A Baba
- Institute of Pathology, University Hospital Essen (AöR), Essen, Germany; Medical Faculty, University of Duisburg-Essen, Essen, Germany
| | - S Kasper
- Department of Medical Oncology, University Hospital Essen (AöR), Essen, Germany; West German Cancer Center, University Hospital Essen (AöR), Essen, Germany; German Cancer Consortium (DKTK), Partner site University Hospital Essen (AöR), Essen, Germany; Medical Faculty, University of Duisburg-Essen, Essen, Germany
| | - J T Siveke
- Department of Medical Oncology, University Hospital Essen (AöR), Essen, Germany; West German Cancer Center, University Hospital Essen (AöR), Essen, Germany; German Cancer Consortium (DKTK), Partner site University Hospital Essen (AöR), Essen, Germany; Bridge Institute of Experimental Tumor Therapy, West German Cancer Center, University Hospital Essen, University of Duisburg-Essen, Essen, Germany; Division of Solid Tumor Translational Oncology, German Cancer Consortium (DKTK Partner Site Essen) and German Cancer Research Center, DKFZ, Heidelberg, Germany; Medical Faculty, University of Duisburg-Essen, Essen, Germany
| | - F Nensa
- Institute for Artificial Intelligence in Medicine, University Hospital Essen (AöR), Essen, Germany; German Cancer Consortium (DKTK), Partner site University Hospital Essen (AöR), Essen, Germany; Institute for Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen (AöR), Essen, Germany; Medical Faculty, University of Duisburg-Essen, Essen, Germany
| | - M Schuler
- Department of Medical Oncology, University Hospital Essen (AöR), Essen, Germany; West German Cancer Center, University Hospital Essen (AöR), Essen, Germany; Medical Faculty, University of Duisburg-Essen, Essen, Germany; National Center for Tumor Diseases (NCT), NCT West, Essen, Germany
| | - G Kaissis
- Institute of Diagnostic and Interventional Radiology, Technical University of Munich, School of Medicine, Munich, Germany; Artificial Intelligence in Healthcare and Medicine, School of Computation, Information and Technology, Technical University of Munich, Munich, Germany
| | - J Kleesiek
- Institute for Artificial Intelligence in Medicine, University Hospital Essen (AöR), Essen, Germany; West German Cancer Center, University Hospital Essen (AöR), Essen, Germany; German Cancer Consortium (DKTK), Partner site University Hospital Essen (AöR), Essen, Germany; Medical Faculty, University of Duisburg-Essen, Essen, Germany
| | - R Braren
- Institute of Diagnostic and Interventional Radiology, Technical University of Munich, School of Medicine, Munich, Germany; German Cancer Consortium (DKTK), Munich partner site, Heidelberg, Germany
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18
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Parmar V, Haubold J, Salhöfer L, Meetschen M, Wrede K, Glas M, Guberina M, Blau T, Bos D, Kureishi A, Hosch R, Nensa F, Forsting M, Deuschl C, Umutlu L. Fully automated MR-based virtual biopsy of primary CNS lymphomas. Neurooncol Adv 2024; 6:vdae022. [PMID: 38516329 PMCID: PMC10956963 DOI: 10.1093/noajnl/vdae022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/23/2024] Open
Abstract
Background Primary central nervous system lymphomas (PCNSL) pose a challenge as they may mimic gliomas on magnetic resonance imaging (MRI) imaging, compelling precise differentiation for appropriate treatment. This study focuses on developing an automated MRI-based workflow to distinguish between PCNSL and gliomas. Methods MRI examinations of 240 therapy-naive patients (141 males and 99 females, mean age: 55.16 years) with cerebral gliomas and PCNSLs (216 gliomas and 24 PCNSLs), each comprising a non-contrast T1-weighted, fluid-attenuated inversion recovery (FLAIR), and contrast-enhanced T1-weighted sequence were included in the study. HD-GLIO, a pre-trained segmentation network, was used to generate segmentations automatically. To validate the segmentation efficiency, 237 manual segmentations were prepared (213 gliomas and 24 PCNSLs). Subsequently, radiomics features were extracted following feature selection and training of an XGBoost algorithm for classification. Results The segmentation models for gliomas and PCNSLs achieved a mean Sørensen-Dice coefficient of 0.82 and 0.80 for whole tumors, respectively. Three classification models were developed in this study to differentiate gliomas from PCNSLs. The first model differentiated PCNSLs from gliomas, with an area under the curve (AUC) of 0.99 (F1-score: 0.75). The second model discriminated between high-grade gliomas and PCNSLs with an AUC of 0.91 (F1-score: 0.6), and the third model differentiated between low-grade gliomas and PCNSLs with an AUC of 0.95 (F1-score: 0.89). Conclusions This study serves as a pilot investigation presenting an automated virtual biopsy workflow that distinguishes PCNSLs from cerebral gliomas. Prior to clinical use, it is necessary to validate the results in a prospective multicenter setting with a larger number of PCNSL patients.
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Affiliation(s)
- Vicky Parmar
- Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany
- Institute for Artificial Intelligence in Medicine, University Hospital Essen, Essen, Germany
| | - Johannes Haubold
- Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany
- Institute for Artificial Intelligence in Medicine, University Hospital Essen, Essen, Germany
| | - Luca Salhöfer
- Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany
- Institute for Artificial Intelligence in Medicine, University Hospital Essen, Essen, Germany
| | - Mathias Meetschen
- Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany
- Institute for Artificial Intelligence in Medicine, University Hospital Essen, Essen, Germany
| | - Karsten Wrede
- Department of Neurosurgery and Spine Surgery, University Hospital Essen, Essen, Germany
| | - Martin Glas
- Department of Neuropathology, University Hospital Essen, Essen, Germany
| | - Maja Guberina
- Department of Radiotherapy, University Hospital Essen, Essen, Germany
| | - Tobias Blau
- Department of Neurology and Neurooncology, University Hospital Essen, Essen, Germany
| | - Denise Bos
- Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany
| | - Anisa Kureishi
- Institute for Artificial Intelligence in Medicine, University Hospital Essen, Essen, Germany
| | - René Hosch
- Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany
- Institute for Artificial Intelligence in Medicine, University Hospital Essen, Essen, Germany
| | - Felix Nensa
- Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany
- Institute for Artificial Intelligence in Medicine, University Hospital Essen, Essen, Germany
| | - Michael Forsting
- Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany
| | - Cornelius Deuschl
- Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany
| | - Lale Umutlu
- Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany
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19
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Engelke M, Schmidt CS, Baldini G, Parmar V, Hosch R, Borys K, Koitka S, Turki AT, Haubold J, Horn PA, Nensa F. Optimizing platelet transfusion through a personalized deep learning risk assessment system for demand management. Blood 2023; 142:2315-2326. [PMID: 37890142 DOI: 10.1182/blood.2023021172] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Revised: 09/29/2023] [Accepted: 10/17/2023] [Indexed: 10/29/2023] Open
Abstract
ABSTRACT Platelet demand management (PDM) is a resource-consuming task for physicians and transfusion managers of large hospitals. Inpatient numbers and institutional standards play significant roles in PDM. However, reliance on these factors alone commonly results in platelet shortages. Using data from multiple sources, we developed, validated, tested, and implemented a patient-specific approach to support PDM that uses a deep learning-based risk score to forecast platelet transfusions for each hospitalized patient in the next 24 hours. The models were developed using retrospective electronic health record data of 34 809 patients treated between 2017 and 2022. Static and time-dependent features included demographics, diagnoses, procedures, blood counts, past transfusions, hematotoxic medications, and hospitalization duration. Using an expanding window approach, we created a training and live-prediction pipeline with a 30-day input and 24-hour forecast. Hyperparameter tuning determined the best validation area under the precision-recall curve (AUC-PR) score for long short-term memory deep learning models, which were then tested on independent data sets from the same hospital. The model tailored for hematology and oncology patients exhibited the best performance (AUC-PR, 0.84; area under the receiver operating characteristic curve [ROC-AUC], 0.98), followed by a multispecialty model covering all other patients (AUC-PR, 0.73). The model specific to cardiothoracic surgery had the lowest performance (AUC-PR, 0.42), likely because of unexpected intrasurgery bleedings. To our knowledge, this is the first deep learning-based platelet transfusion predictor enabling individualized 24-hour risk assessments at high AUC-PR. Implemented as a decision-support system, deep-learning forecasts might improve patient care by detecting platelet demand earlier and preventing critical transfusion shortages.
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Affiliation(s)
- Merlin Engelke
- Institute for Artificial Intelligence in Medicine, University Medicine Essen, Essen, Germany
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Medicine Essen, Essen, Germany
| | - Cynthia Sabrina Schmidt
- Institute for Artificial Intelligence in Medicine, University Medicine Essen, Essen, Germany
- Institute for Transfusion Medicine, University Medicine Essen, Essen, Germany
| | - Giulia Baldini
- Institute for Artificial Intelligence in Medicine, University Medicine Essen, Essen, Germany
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Medicine Essen, Essen, Germany
| | - Vicky Parmar
- Institute for Artificial Intelligence in Medicine, University Medicine Essen, Essen, Germany
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Medicine Essen, Essen, Germany
| | - René Hosch
- Institute for Artificial Intelligence in Medicine, University Medicine Essen, Essen, Germany
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Medicine Essen, Essen, Germany
| | - Katarzyna Borys
- Institute for Artificial Intelligence in Medicine, University Medicine Essen, Essen, Germany
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Medicine Essen, Essen, Germany
| | - Sven Koitka
- Institute for Artificial Intelligence in Medicine, University Medicine Essen, Essen, Germany
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Medicine Essen, Essen, Germany
| | - Amin T Turki
- Computational Hematology Laboratory, Department of Hematology and Stem Cell Transplantation, West-German Cancer Center, University Medicine Essen, Essen, Germany
- Department of Hematology and Oncology, Marienhospital University Hospital, Ruhr University Bochum, Bochum, Germany
| | - Johannes Haubold
- Institute for Artificial Intelligence in Medicine, University Medicine Essen, Essen, Germany
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Medicine Essen, Essen, Germany
| | - Peter A Horn
- Institute for Transfusion Medicine, University Medicine Essen, Essen, Germany
| | - Felix Nensa
- Institute for Artificial Intelligence in Medicine, University Medicine Essen, Essen, Germany
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Medicine Essen, Essen, Germany
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20
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Hlouschek J, König B, Bos D, Santiago A, Zensen S, Haubold J, Pöttgen C, Herz A, Opitz M, Wetter A, Guberina M, Stuschke M, Zylka W, Kühl H, Guberina N. Experimental Examination of Conventional, Semi-Automatic, and Automatic Volumetry Tools for Segmentation of Pulmonary Nodules in a Phantom Study. Diagnostics (Basel) 2023; 14:28. [PMID: 38201337 PMCID: PMC10804383 DOI: 10.3390/diagnostics14010028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2023] [Revised: 12/10/2023] [Accepted: 12/17/2023] [Indexed: 01/12/2024] Open
Abstract
The aim of this study is to examine the precision of semi-automatic, conventional and automatic volumetry tools for pulmonary nodules in chest CT with phantom N1 LUNGMAN. The phantom is a life-size anatomical chest model with pulmonary nodules representing solid and subsolid metastases. Gross tumor volumes (GTVis) were contoured using various approaches: manually (0); as a means of semi-automated, conventional contouring with (I) adaptive-brush function; (II) flood-fill function; and (III) image-thresholding function. Furthermore, a deep-learning algorithm for automatic contouring was applied (IV). An intermodality comparison of the above-mentioned strategies for contouring GTVis was performed. For the mean GTVref (standard deviation (SD)), the interquartile range (IQR)) was 0.68 mL (0.33; 0.34-1.1). GTV segmentation was distributed as follows: (I) 0.61 mL (0.27; 0.36-0.92); (II) 0.41 mL (0.28; 0.23-0.63); (III) 0.65 mL (0.35; 0.32-0.90); and (IV) 0.61 mL (0.29; 0.33-0.95). GTVref was found to be significantly correlated with GTVis (I) p < 0.001, r = 0.989 (III) p = 0.001, r = 0.916, and (IV) p < 0.001, r = 0.986, but not with (II) p = 0.091, r = 0.595. The Sørensen-Dice indices for the semi-automatic tools were 0.74 (I), 0.57 (II) and 0.71 (III). For the semi-automatic, conventional segmentation tools evaluated, the adaptive-brush function (I) performed closest to the reference standard (0). The automatic deep learning tool (IV) showed high performance for auto-segmentation and was close to the reference standard. For high precision radiation therapy, visual control, and, where necessary, manual correction, are mandatory for all evaluated tools.
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Affiliation(s)
- Julian Hlouschek
- Department of Radiotherapy, West German Cancer Center, University Hospital Essen, Hufelandstrasse 55, 45147 Essen, Germany
| | - Britta König
- Department of Radiology, University Hospital Muenster (UKM), Albert-Schweitzer-Campus 1, Gebäude A1, 48149 Muenster, Germany
| | - Denise Bos
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Hufelandstrasse 55, 45147 Essen, Germany
| | - Alina Santiago
- Department of Radiotherapy, West German Cancer Center, University Hospital Essen, Hufelandstrasse 55, 45147 Essen, Germany
| | - Sebastian Zensen
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Hufelandstrasse 55, 45147 Essen, Germany
| | - Johannes Haubold
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Hufelandstrasse 55, 45147 Essen, Germany
| | - Christoph Pöttgen
- Department of Radiotherapy, West German Cancer Center, University Hospital Essen, Hufelandstrasse 55, 45147 Essen, Germany
| | - Andreas Herz
- Department of Radiotherapy, West German Cancer Center, University Hospital Essen, Hufelandstrasse 55, 45147 Essen, Germany
| | - Marcel Opitz
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Hufelandstrasse 55, 45147 Essen, Germany
| | - Axel Wetter
- Department of Diagnostic and Interventional Radiology, Neuroradiology, Asklepios Klinikum Harburg, Eißendorfer Pferdeweg 52, 21075 Hamburg, Germany
| | - Maja Guberina
- Department of Radiotherapy, West German Cancer Center, University Hospital Essen, Hufelandstrasse 55, 45147 Essen, Germany
| | - Martin Stuschke
- Department of Radiotherapy, West German Cancer Center, University Hospital Essen, Hufelandstrasse 55, 45147 Essen, Germany
| | - Waldemar Zylka
- Westphalian University, Campus Gelsenkirchen, Neidenburger Str. 43, 45897 Gelsenkirchen, Germany
| | - Hilmar Kühl
- Department of Radiology, St. Bernhard-Hospital Kamp-Lintfort, Bürgermeister-Schmelzing-Str. 90, 47475 Kamp-Lintfort, Germany
| | - Nika Guberina
- Department of Radiotherapy, West German Cancer Center, University Hospital Essen, Hufelandstrasse 55, 45147 Essen, Germany
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21
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Milosevic A, Styczen H, Haubold J, Kessler L, Grueneisen J, Li Y, Weber M, Fendler WP, Morawitz J, Damman P, Wrede K, Kebir S, Glas M, Guberina M, Blau T, Schaarschmidt BM, Deuschl C. Correlation of the apparent diffusion coefficient with the standardized uptake value in meningioma of the skull plane using [68]Ga-DOTATOC PET/MRI. Nucl Med Commun 2023; 44:1106-1113. [PMID: 37823259 DOI: 10.1097/mnm.0000000000001774] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/13/2023]
Abstract
PURPOSE To evaluate a correlation between an MRI-specific marker for cellular density [apparent diffusion coefficient (ADC)] and the expression of Somatostatin Receptors (SSTR) in patients with meningioma of the skull plane and orbital space. METHODS 68 Ga-DOTATOC PET/MR imaging was performed in 60 Patients with suspected or diagnosed meningiomas of the skull base and eye socket. Analysis of ADC values succeeded in 32 patients. ADC values (ADC mean and ADC min ) were analyzed using a polygonal region of interest. Tracer-uptake of target lesions was assessed according to corresponding maximal (SUV max ) and mean (SUV mean ) values. Correlations between assessed parameters were evaluated using the Pearson correlation coefficient. RESULTS One out of 32 patients (3%) was diagnosed with lymphoma by histopathological examination and therefore excluded from further analysis. Median ADC mean amounted to 822 × 10 -5 mm²/s -1 (95% CI: 570-1497) and median ADC min was 493 × 10 -5 mm 2 /s -1 (95% CI: 162-783). There were no significant correlations between SUV max and ADC min (r = 0.60; P = 0.76) or ADC mean (r = -0.52; P = 0.79), respectively. However, Pearson's test showed a weak, inverse but insignificant correlation between ADC mean and SUV mean (r = -0.33; P = 0.07). CONCLUSION The presented data displays no relevant correlations between increased SSTR expression and cellularity in patients with meningioma of the skull base. SSTR-PET and DWI thus may offer complementary information on tumor characteristics of meningioma.
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Affiliation(s)
- Aleksandar Milosevic
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen,
| | - Hanna Styczen
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen,
| | - Johannes Haubold
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen,
| | - Lukas Kessler
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen,
| | - Johannes Grueneisen
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen,
| | - Yan Li
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen,
| | - Manuel Weber
- Department of Nuclear Medicine, University Hospital Essen,
| | | | | | - Philipp Damman
- Department of Neurosurgery and Spine Surgery, University Hospital Essen,
| | - Karsten Wrede
- Department of Neurosurgery and Spine Surgery, University Hospital Essen,
| | - Sied Kebir
- Department of Neurology and Neurooncology, University Hospital Essen,
| | - Martin Glas
- Department of Neurology and Neurooncology, University Hospital Essen,
| | - Maja Guberina
- Department of Radiotherapy, University Hospital Essen and
| | - Tobias Blau
- Department of Neuropathology, University Hospital Essen, Germany
| | - Benedikt M Schaarschmidt
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen,
| | - Cornelius Deuschl
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen,
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22
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Zensen S, Bücker A, Meetschen M, Haubold J, Opitz M, Theysohn JM, Schramm S, Jochheim L, Kasper S, Forsting M, Schaarschmidt BM. Current use of percutaneous image-guided tumor ablation for the therapy of liver tumors: lessons learned from the registry of the German Society for Interventional Radiology and Minimally Invasive Therapy (DeGIR) 2018-2022. Eur Radiol 2023:10.1007/s00330-023-10412-w. [PMID: 37935847 DOI: 10.1007/s00330-023-10412-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Revised: 08/31/2023] [Accepted: 10/05/2023] [Indexed: 11/09/2023]
Abstract
OBJECTIVES Percutaneous image-guided tumor ablation of liver malignancies has become an indispensable therapeutic procedure. The aim of this evaluation of the prospectively managed multinational registry of the voluntary German Society for Interventional Radiology and Minimally Invasive Therapy (DeGIR) was to analyze its use, technical success, and complications in clinical practice. MATERIALS AND METHODS All liver tumor ablations from 2018 to 2022 were included. Technical success was defined as complete ablation of the tumor with an ablative margin. RESULTS A total of 7228 liver tumor ablations from 136 centers in Germany and Austria were analyzed. In total, 31.4% (2268/7228) of patients were female. Median age was 67 years (IQR 58-74 years). Microwave ablation (MWA) was performed in 65.1% (4703/7228), and radiofrequency ablation (RFA) in 32.7% (2361/7228). Of 5229 cases with reported tumor etiology, 60.3% (3152/5229) of ablations were performed for liver metastases and 37.3% (1950/5229) for hepatocellular carcinoma. The median lesion diameter was 19 mm (IQR 12-27 mm). In total, 91.8% (6636/7228) of ablations were technically successful. The rate of technically successful ablations was significantly higher in MWA (93.9%, 4417/4703) than in RFA (87.3%, 2061/2361) (p < 0.0001). The total complication rate was 3.0% (214/7228) and was significantly higher in MWA (4.0%, 189/4703) than in RFA (0.9%, 21/2361, p < 0.0001). Additional needle track ablation did not increase the rate of major complications significantly (24.8% (33/133) vs. 28.4% (23/81), p = 0.56)). CONCLUSION MWA is the most frequent ablation method. Percutaneous image-guided liver tumor ablations have a high technical success rate, which is higher for MWA than RFA. The complication rate is generally low but is higher for MWA than RFA. CLINICAL RELEVANCE STATEMENT Percutaneous image-guided liver ablation using microwave ablation and radiofrequency ablation are effective therapeutic procedures with low complication rates for the treatment of primary and secondary liver malignancies. KEY POINTS • Percutaneous image-guided liver tumor ablations have a high technical success rate, which is higher for microwave ablation than radiofrequency ablation. • Microwave ablation is the most frequent ablation method ahead of radiofrequency ablation. • The complication rate is generally low but is higher for microwave ablation than radiofrequency ablation.
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Affiliation(s)
- Sebastian Zensen
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany.
| | - Arno Bücker
- Department of Diagnostic and Interventional Radiology, University Hospital Homburg/Saar, Homburg, Germany
| | - Mathias Meetschen
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany
| | - Johannes Haubold
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany
| | - Marcel Opitz
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany
| | - Jens M Theysohn
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany
| | - Sara Schramm
- Institute for Medical Informatics, Biometry, and Epidemiology, University Hospital Essen, Essen, Germany
| | - Leonie Jochheim
- Department of Gastroenterology and Hepatology, University Hospital Essen, Essen, Germany
| | - Stefan Kasper
- West German Cancer Center, Department of Medical Oncology, University Hospital Essen, University of Duisburg-Essen, Essen, Germany
| | - Michael Forsting
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany
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23
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Wu D, Jin Y, Xing Y, Abate MD, Abbasian M, Abbasi-Kangevari M, Abbasi-Kangevari Z, Abd-Allah F, Abdelmasseh M, Abdollahifar MA, Abdulah DM, Abedi A, Abedi V, Abidi H, Aboagye RG, Abolhassani H, Abuabara K, Abyadeh M, Addo IY, Adeniji KN, Adepoju AV, Adesina MA, Sakilah Adnani QE, Afarideh M, Aghamiri S, Agodi A, Agrawal A, Aguilera Arriagada CE, Ahmad A, Ahmad D, Ahmad S, Ahmad S, Ahmadi A, Ahmed A, Ahmed A, Aithala JP, Ajadi AA, Ajami M, Akbarzadeh-Khiavi M, Alahdab F, AlBataineh MT, Alemi S, Saeed Al-Gheethi AA, Ali L, Alif SM, Almazan JU, Almustanyir S, Alqahtani JS, Alqasmi I, Khan Altaf IU, Alvis-Guzman N, Alvis-Zakzuk NJ, Al-Worafi YM, Aly H, Amani R, Amu H, Amusa GA, Andrei CL, Ansar A, Ansariniya H, Anyasodor AE, Arabloo J, Arefnezhad R, Arulappan J, Asghari-Jafarabadi M, Ashraf T, Atata JA, Athari SS, Atlaw D, Wahbi Atout MM, Aujayeb A, Awan AT, Ayatollahi H, Azadnajafabad S, Azzam AY, Badawi A, Badiye AD, Bagherieh S, Baig AA, Bantie BB, Barchitta M, Bardhan M, Barker-Collo SL, Barone-Adesi F, Batra K, Bayileyegn NS, Behnoush AH, Belgaumi UI, Bemanalizadeh M, Bensenor IM, Beyene KA, Bhagavathula AS, Bhardwaj P, Bhaskar S, Bhat AN, Bitaraf S, Bitra VR, Boloor A, Bora K, Botelho JS, Buchbinder R, Calina D, Cámera LA, Carvalho AF, Kai Chan JS, Chattu VK, Abebe EC, Chichagi F, Choi S, Chou TC, Chu DT, Coberly K, Costa VM, Couto RA, Cruz-Martins N, Dadras O, Dai X, Damiani G, Dascalu AM, Dashti M, Debela SA, Dellavalle RP, Demetriades AK, Demlash AA, Deng X, Desai HD, Desai R, Rahman Dewan SM, Dey S, Dharmaratne SD, Diaz D, Dibas M, Dinis-Oliveira RJ, Diress M, Do TC, Doan DK, Dodangeh M, Dodangeh M, Dongarwar D, Dube J, Dziedzic AM, Ed-Dra A, Edinur HA, Eissazade N, Ekholuenetale M, Ekundayo TC, Elemam NM, Elhadi M, Elmehrath AO, Abdou Elmeligy OA, Emamverdi M, Emeto TI, Esayas HL, Eshetu HB, Etaee F, Fagbamigbe AF, Faghani S, Fakhradiyev IR, Fatehizadeh A, Fathi M, Feizkhah A, Fekadu G, Fereidouni M, Fereshtehnejad SM, Fernandes JC, Ferrara P, Fetensa G, Filip I, Fischer F, Foroutan B, Foroutan M, Fukumoto T, Ganesan B, Belete Gemeda BN, Ghamari SH, Ghasemi M, Gholamalizadeh M, Gill TK, Gillum RF, Goldust M, Golechha M, Goleij P, Golinelli D, Goudarzi H, Guan SY, Guo Y, Gupta B, Gupta VB, Gupta VK, Haddadi R, Hadi NR, Halwani R, Haque S, Hasan I, Hashempour R, Hassan A, Hassan TS, Hassanzadeh S, Hassen MB, Haubold J, Hayat K, Heidari G, Heidari M, Heidari-Soureshjani R, Herteliu C, Hessami K, Hezam K, Hiraike Y, Holla R, Hosseini MS, Huynh HH, Hwang BF, Ibitoye SE, Ilic IM, Ilic MD, Iranmehr A, Iravanpour F, Ismail NE, Iwagami M, Iwu CC, Jacob L, Jafarinia M, Jafarzadeh A, Jahankhani K, Jahrami H, Jakovljevic M, Jamshidi E, Jani CT, Janodia MD, Jayapal SK, Jayaram S, Jeganathan J, Jonas JB, Joseph A, Joseph N, Joshua CE, Vaishali K, Kaambwa B, Kabir A, Kabir Z, Kadashetti V, Kaliyadan F, Kalroozi F, Kamal VK, Kandel A, Kandel H, Kanungo S, Karami J, Karaye IM, Karimi H, Kasraei H, Kazemian S, Kebede SA, Keikavoosi-Arani L, Keykhaei M, Khader YS, Khajuria H, Khamesipour F, Khan EA, Khan IA, Khan M, Khan MJ, Khan MA, Khan MA, Khatatbeh H, Khatatbeh MM, Khateri S, Khayat Kashani HR, Kim MS, Kisa A, Kisa S, Koh HY, Kolkhir P, Korzh O, Kotnis AL, Koul PA, Koyanagi A, Krishan K, Kuddus M, Kulkarni VV, Kumar N, Kundu S, Kurmi OP, La Vecchia C, Lahariya C, Laksono T, Lám J, Latief K, Lauriola P, Lawal BK, Thu Le TT, Bich Le TT, Lee M, Lee SW, Lee WC, Lee YH, Lenzi J, Levi M, Li W, Ligade VS, Lim SS, Liu G, Liu X, Llanaj E, Lo CH, Machado VS, Maghazachi AA, Mahmoud MA, Mai TA, Majeed A, Sanaye PM, Makram OM, Rad EM, Malhotra K, Malik AA, Malik I, Mallhi TH, Malta DC, Mansournia MA, Mantovani LG, Martorell M, Masoudi S, Masoumi SZ, Mathangasinghe Y, Mathews E, Mathioudakis AG, Maugeri A, Mayeli M, Carabeo Medina JR, Meles GG, Mendes JJ, Menezes RG, Mestrovic T, Michalek IM, Micheletti Gomide Nogueira de Sá AC, Mihretie ET, Nhat Minh LH, Mirfakhraie R, Mirrakhimov EM, Misganaw A, Mohamadkhani A, Mohamed NS, Mohammadi F, Mohammadi S, Mohammed S, Mohammed S, Mohan S, Mohseni A, Mokdad AH, Momtazmanesh S, Monasta L, Moni MA, Moniruzzaman M, Moradi Y, Morovatdar N, Mostafavi E, Mousavi P, Mukoro GD, Mulita A, Mulu GB, Murillo-Zamora E, Musaigwa F, Mustafa G, Muthu S, Nainu F, Nangia V, Swamy SN, Natto ZS, Navaraj P, Nayak BP, Nazri-Panjaki A, Negash H, Nematollahi MH, Nguyen DH, Hien Nguyen HT, Nguyen HQ, Nguyen PT, Nguyen VT, Niazi RK, Nikolouzakis TK, Nnyanzi LA, Noreen M, Nzoputam CI, Nzoputam OJ, Oancea B, Oh IH, Okati-Aliabad H, Okonji OC, Okwute PG, Olagunju AT, Olatubi MI, Olufadewa II, Ordak M, Otstavnov N, Owolabi MO, Mahesh P, Padubidri JR, Pak A, Pakzad R, Palladino R, Pana A, Pantazopoulos I, Papadopoulou P, Pardhan S, Parthasarathi A, Pashaei A, Patel J, Pathan AR, Patil S, Paudel U, Pawar S, Pedersini P, Pensato U, Pereira DM, Pereira J, Pereira MO, Pereira RB, Peres MF, Perianayagam A, Perna S, Petcu IR, Pezeshki PS, Pham HT, Philip AK, Piradov MA, Podder I, Podder V, Poddighe D, Sady Prates EJ, Qattea I, Radfar A, Raee P, Rafiei A, Raggi A, Rahim F, Rahimi M, Rahimifard M, Rahimi-Movaghar V, Rahman MO, Ur Rahman MH, Rahman M, Rahman MA, Rahmani AM, Rahmani M, Rahmani S, Rahmanian V, Ramasubramani P, Rancic N, Rao IR, Rashedi S, Rashid AM, Ravikumar N, Rawaf S, Mohamed Redwan EM, Rezaei N, Rezaei N, Rezaei N, Rezaeian M, Ribeiro D, Rodrigues M, Buendia Rodriguez JA, Roever L, Romero-Rodríguez E, Saad AM, Saddik B, Sadeghian S, Saeed U, Safary A, Safdarian M, Safi SZ, Saghazadeh A, Sagoe D, Sharif-Askari FS, Sharif-Askari NS, Sahebkar A, Sahoo H, Sahraian MA, Sajid MR, Sakhamuri S, Sakshaug JW, Saleh MA, Salehi L, Salehi S, Farrokhi AS, Samadzadeh S, Samargandy S, Samieefar N, Samy AM, Sanadgol N, Sanjeev RK, Sawhney M, Saya GK, Schuermans A, Senthilkumaran S, Sepanlou SG, Sethi Y, Shafie M, Shah H, Shahid I, Shahid S, Shaikh MA, Sharfaei S, Sharma M, Shayan M, Shehata HS, Sheikh A, Shetty JK, Shin JI, Shirkoohi R, Shitaye NA, Shivakumar K, Shivarov V, Shobeiri P, Siabani S, Sibhat MM, Siddig EE, Simpson CR, Sinaei E, Singh H, Singh I, Singh JA, Singh P, Singh S, Siraj MS, Al Mamun Sohag A, Solanki R, Solikhah S, Solomon Y, Soltani-Zangbar MS, Sun J, Szeto MD, Tabarés-Seisdedos R, Tabatabaei SM, Tabish M, Taheri E, Tahvildari A, Talaat IM, Lukenze Tamuzi JJ, Tan KK, Tat NY, Oliaee RT, Tavasol A, Temsah MH, Thangaraju P, Tharwat S, Tibebu NS, Vera Ticoalu JH, Tillawi T, Tiruye TY, Tiyuri A, Tovani-Palone MR, Tripathi M, Tsegay GM, Tualeka AR, Ty SS, Ubah CS, Ullah S, Ullah S, Umair M, Umakanthan S, Upadhyay E, Vahabi SM, Vaithinathan AG, Tahbaz SV, Valizadeh R, Varthya SB, Vasankari TJ, Venketasubramanian N, Verras GI, Villafañe JH, Vlassov V, Vo DC, Waheed Y, Waris A, Welegebrial BG, Westerman R, Wickramasinghe DP, Wickramasinghe ND, Willekens B, Woldegeorgis BZ, Woldemariam M, Xiao H, Yada DY, Yahya G, Yang L, Yazdanpanah F, Yon DK, Yonemoto N, You Y, Zahir M, Zaidi SS, Zangiabadian M, Zare I, Zeineddine MA, Zemedikun DT, Zeru NG, Zhang C, Zhao H, Zhong C, Zielińska M, Zoladl M, Zumla A, Guo C, Tam LS. Global, regional, and national incidence of six major immune-mediated inflammatory diseases: findings from the global burden of disease study 2019. EClinicalMedicine 2023; 64:102193. [PMID: 37731935 PMCID: PMC10507198 DOI: 10.1016/j.eclinm.2023.102193] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/07/2023] [Revised: 08/15/2023] [Accepted: 08/16/2023] [Indexed: 09/22/2023] Open
Abstract
Background The causes for immune-mediated inflammatory diseases (IMIDs) are diverse and the incidence trends of IMIDs from specific causes are rarely studied. The study aims to investigate the pattern and trend of IMIDs from 1990 to 2019. Methods We collected detailed information on six major causes of IMIDs, including asthma, inflammatory bowel disease, multiple sclerosis, rheumatoid arthritis, psoriasis, and atopic dermatitis, between 1990 and 2019, derived from the Global Burden of Disease study in 2019. The average annual percent change (AAPC) in number of incidents and age standardized incidence rate (ASR) on IMIDs, by sex, age, region, and causes, were calculated to quantify the temporal trends. Findings In 2019, rheumatoid arthritis, atopic dermatitis, asthma, multiple sclerosis, psoriasis, inflammatory bowel disease accounted 1.59%, 36.17%, 54.71%, 0.09%, 6.84%, 0.60% of overall new IMIDs cases, respectively. The ASR of IMIDs showed substantial regional and global variation with the highest in High SDI region, High-income North America, and United States of America. Throughout human lifespan, the age distribution of incident cases from six IMIDs was quite different. Globally, incident cases of IMIDs increased with an AAPC of 0.68 and the ASR decreased with an AAPC of -0.34 from 1990 to 2019. The incident cases increased across six IMIDs, the ASR of rheumatoid arthritis increased (0.21, 95% CI 0.18, 0.25), while the ASR of asthma (AAPC = -0.41), inflammatory bowel disease (AAPC = -0.72), multiple sclerosis (AAPC = -0.26), psoriasis (AAPC = -0.77), and atopic dermatitis (AAPC = -0.15) decreased. The ASR of overall and six individual IMID increased with SDI at regional and global level. Countries with higher ASR in 1990 experienced a more rapid decrease in ASR. Interpretation The incidence patterns of IMIDs varied considerably across the world. Innovative prevention and integrative management strategy are urgently needed to mitigate the increasing ASR of rheumatoid arthritis and upsurging new cases of other five IMIDs, respectively. Funding The Global Burden of Disease Study is funded by the Bill and Melinda Gates Foundation. The project funded by Scientific Research Fund of Sichuan Academy of Medical Sciences & Sichuan Provincial People's Hospital (2022QN38).
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Tamulevicius M, Oezcelik A, Koitka S, Theysohn JM, Hoyer DP, Farzaliyev F, Haubold J, Nensa F, Treckmann J, Malamutmann E. Preoperative Computed Tomography Volumetry and Graft Weight Estimation of Left Lateral Segment in Pediatric Living Donor Liver Transplant. EXP CLIN TRANSPLANT 2023; 21:831-836. [PMID: 37965959 DOI: 10.6002/ect.2023.0176] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2023]
Abstract
OBJECTIVES Liver volumetry based on a computed tomography scan is widely used to estimate liver volume before any liver resection, especially before living donorliver donation. The 1-to-1 conversion rule for liver volume to liver weight has been widely adopted; however, debate continues regarding this approach. Therefore, we analyzed the relationship between the left-lateral lobe liver graft volume and actual graft weight. MATERIALS AND METHODS This study retrospectively included consecutive donors who underwent left lateral hepatectomy for pediatric living donor liver transplant from December 2008 to September 2020. All donors were healthy adults who met the evaluation criteria for pediatric living donor liver transplant and underwent a preoperative contrast-enhanced computed tomography scan. Manual segmentation of the leftlateral liverlobe for graft volume estimation and intraoperative measurement of an actual graft weight were performed. The relationship between estimated graft volume and actual graft weight was analyzed. RESULTS Ninety-four living liver donors were included in the study. The mean actual graft weight was ~283.4 ± 68.5 g, and the mean graft volume was 244.9 ± 63.86 mL. A strong correlation was shown between graft volume and actual graft weight (r = 0.804; P < .001). Bland-Altman analysis revealed an interobserver agreement of 38.0 ± 97.25, and intraclass correlation coefficient showed almost perfect agreement(r = 0.840; P < .001). The conversion formula for calculating graft weight based on computed tomography volumetry was determined based on regression analysis: 0.88 × graft volume + 41.63. CONCLUSIONS The estimation of left liver graft weight using only the 1-to-1 rule is subject to measurable variability in calculated graft weights and tends to underestimate the true graft weight. Instead, a different, improved conversion formula should be used to calculate graft weight to more accurately determine donor graft weight-to-recipient body weightratio and reduce the risk of underestimation of liver graft weightin the donor selection process before pediatric living donor liver transplant.
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Affiliation(s)
- Martynas Tamulevicius
- From the University Hospital Essen, Department of General, Visceral and Transplantation Surgery, Essen, Germany
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Meetschen M, Bücker A, Nikolaou K, Salhöfer L, Zensen S, Schaarschmidt BM, Wetter A, Haubold J. Complications Of Image-Guided Drainage. Dtsch Arztebl Int 2023; 120:553-554. [PMID: 37732592 PMCID: PMC10546882 DOI: 10.3238/arztebl.m2023.0140] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/09/2022] [Revised: 05/23/2023] [Accepted: 05/23/2023] [Indexed: 09/22/2023]
Affiliation(s)
- Mathias Meetschen
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany
| | - Arno Bücker
- Department of Diagnostic and Interventional Radiology, Saarland University Hospital, Homburg, Germany
| | - Konstantin Nikolaou
- Department of Diagnostic and Interventional Radiology, University Hospital Tübingen, Tübingen, Germany
| | - Luca Salhöfer
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany
| | - Sebastian Zensen
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany
| | - Benedikt M. Schaarschmidt
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany
| | - Axel Wetter
- Department of Diagnostic and Interventional Radiology, Neuroradiology, Asklepios Klinikum Harburg, Hamburg, Germany
| | - Johannes Haubold
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany
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Fischer M, Küstner T, Pappa S, Niendorf T, Pischon T, Kröncke T, Bette S, Schramm S, Schmidt B, Haubold J, Nensa F, Nonnenmacher T, Palm V, Bamberg F, Kiefer L, Schick F, Yang B. Identification of radiomic biomarkers in a set of four skeletal muscle groups on Dixon MRI of the NAKO MR study. BMC Med Imaging 2023; 23:104. [PMID: 37553619 PMCID: PMC10408104 DOI: 10.1186/s12880-023-01056-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Accepted: 07/18/2023] [Indexed: 08/10/2023] Open
Abstract
In this work, we propose a processing pipeline for the extraction and identification of meaningful radiomics biomarkers in skeletal muscle tissue as displayed using Dixon-weighted MRI. Diverse and robust radiomics features can be identified that may be of aid in the accurate quantification e.g. varying degrees of sarcopenia in respective muscles of large cohorts. As such, the approach comprises the texture feature extraction from raw data based on well established approaches, such as a nnU-Net neural network and the Pyradiomics toolbox, a subsequent selection according to adequate conditions for the muscle tissue of the general population, and an importance-based ranking to further narrow the amount of meaningful features with respect to auxiliary targets. The performance was investigated with respect to the included auxiliary targets, namely age, body mass index (BMI), and fat fraction (FF). Four skeletal muscles with different fiber architecture were included: the mm. glutaei, m. psoas, as well as the extensors and adductors of the thigh. The selection allowed for a reduction from 1015 available texture features to 65 for age, 53 for BMI, and 36 for FF from the available fat/water contrast images considering all muscles jointly. Further, the dependence of the importance rankings calculated for the auxiliary targets on validation sets (in a cross-validation scheme) was investigated by boxplots. In addition, significant differences between subgroups of respective auxiliary targets as well as between both sexes were shown to be present within the ten lowest ranked features by means of Kruskal-Wallis H-tests and Mann-Whitney U-tests. The prediction performance for the selected features and the ranking scheme were verified on validation sets by a random forest based multi-class classification, with strong area under the curve (AUC) values of the receiver operator characteristic (ROC) of 73.03 ± 0.70 % and 73.63 ± 0.70 % for the water and fat images in age, 80.68 ± 0.30 % and 88.03 ± 0.89 % in BMI, as well as 98.36 ± 0.03 % and 98.52 ± 0.09 % in FF.
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Affiliation(s)
- Marc Fischer
- Institute of Signal Processing and System Theory, University of Stuttgart, Stuttgart, Germany
| | - Thomas Küstner
- Medical Image and Data Analysis (MIDAS.lab), University Hospital Tübingen, Tübingen, Germany.
| | - Sofia Pappa
- Section on Experimental Radiology, University Hospital Tübingen, Tübingen, Germany
| | - Thoralf Niendorf
- Berlin Ultrahigh Field Facility (B.U.F.F.), Max-Delbrück-Center for Molecular Medicine, Berlin, Germany
| | - Tobias Pischon
- Berlin Ultrahigh Field Facility (B.U.F.F.), Max-Delbrück-Center for Molecular Medicine, Berlin, Germany
| | - Thomas Kröncke
- Department of Diagnostic and Interventional Radiology, University Hospital Augsburg, Augsburg, Germany
- Centre for Advanced Analytics and Predictive Sciences (CAAPS), University Augsburg, Augsburg, Germany
| | - Stefanie Bette
- Department of Diagnostic and Interventional Radiology, University Hospital Augsburg, Augsburg, Germany
| | - Sara Schramm
- Institute for Medical Informatics, Biometry and Epidemiology, Essen University Hospital, Essen, Germany
| | - Börge Schmidt
- Institute for Medical Informatics, Biometry and Epidemiology, Essen University Hospital, Essen, Germany
| | | | | | | | | | | | - Lena Kiefer
- Department of Radiology, University Hospital Tübingen, Tübingen, Germany
| | - Fritz Schick
- Section on Experimental Radiology, University Hospital Tübingen, Tübingen, Germany
| | - Bin Yang
- Institute of Signal Processing and System Theory, University of Stuttgart, Stuttgart, Germany
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Ong KL, Stafford LK, McLaughlin SA, Boyko EJ, Vollset SE, Smith AE, Dalton BE, Duprey J, Cruz JA, Hagins H, Lindstedt PA, Aali A, Abate YH, Abate MD, Abbasian M, Abbasi-Kangevari Z, Abbasi-Kangevari M, Abd ElHafeez S, Abd-Rabu R, Abdulah DM, Abdullah AYM, Abedi V, Abidi H, Aboagye RG, Abolhassani H, Abu-Gharbieh E, Abu-Zaid A, Adane TD, Adane DE, Addo IY, Adegboye OA, Adekanmbi V, Adepoju AV, Adnani QES, Afolabi RF, Agarwal G, Aghdam ZB, Agudelo-Botero M, Aguilera Arriagada CE, Agyemang-Duah W, Ahinkorah BO, Ahmad D, Ahmad R, Ahmad S, Ahmad A, Ahmadi A, Ahmadi K, Ahmed A, Ahmed A, Ahmed LA, Ahmed SA, Ajami M, Akinyemi RO, Al Hamad H, Al Hasan SM, AL-Ahdal TMA, Alalwan TA, Al-Aly Z, AlBataineh MT, Alcalde-Rabanal JE, Alemi S, Ali H, Alinia T, Aljunid SM, Almustanyir S, Al-Raddadi RM, Alvis-Guzman N, Amare F, Ameyaw EK, Amiri S, Amusa GA, Andrei CL, Anjana RM, Ansar A, Ansari G, Ansari-Moghaddam A, Anyasodor AE, Arabloo J, Aravkin AY, Areda D, Arifin H, Arkew M, Armocida B, Ärnlöv J, Artamonov AA, Arulappan J, Aruleba RT, Arumugam A, Aryan Z, Asemu MT, Asghari-Jafarabadi M, Askari E, Asmelash D, Astell-Burt T, Athar M, Athari SS, Atout MMW, Avila-Burgos L, Awaisu A, Azadnajafabad S, B DB, Babamohamadi H, Badar M, Badawi A, Badiye AD, Baghcheghi N, Bagheri N, Bagherieh S, Bah S, Bahadory S, Bai R, Baig AA, Baltatu OC, Baradaran HR, Barchitta M, Bardhan M, Barengo NC, Bärnighausen TW, Barone MTU, Barone-Adesi F, Barrow A, Bashiri H, Basiru A, Basu S, Basu S, Batiha AMM, Batra K, Bayih MT, Bayileyegn NS, Behnoush AH, Bekele AB, Belete MA, Belgaumi UI, Belo L, Bennett DA, Bensenor IM, Berhe K, Berhie AY, Bhaskar S, Bhat AN, Bhatti JS, Bikbov B, Bilal F, Bintoro BS, Bitaraf S, Bitra VR, Bjegovic-Mikanovic V, Bodolica V, Boloor A, Brauer M, Brazo-Sayavera J, Brenner H, Butt ZA, Calina D, Campos LA, Campos-Nonato IR, Cao Y, Cao C, Car J, Carvalho M, Castañeda-Orjuela CA, Catalá-López F, Cerin E, Chadwick J, Chandrasekar EK, Chanie GS, Charan J, Chattu VK, Chauhan K, Cheema HA, Chekol Abebe E, Chen S, Cherbuin N, Chichagi F, Chidambaram SB, Cho WCS, Choudhari SG, Chowdhury R, Chowdhury EK, Chu DT, Chukwu IS, Chung SC, Coberly K, Columbus A, Contreras D, Cousin E, Criqui MH, Cruz-Martins N, Cuschieri S, Dabo B, Dadras O, Dai X, Damasceno AAM, Dandona R, Dandona L, Das S, Dascalu AM, Dash NR, Dashti M, Dávila-Cervantes CA, De la Cruz-Góngora V, Debele GR, Delpasand K, Demisse FW, Demissie GD, Deng X, Denova-Gutiérrez E, Deo SV, Dervišević E, Desai HD, Desale AT, Dessie AM, Desta F, Dewan SMR, Dey S, Dhama K, Dhimal M, Diao N, Diaz D, Dinu M, Diress M, Djalalinia S, Doan LP, Dongarwar D, dos Santos Figueiredo FW, Duncan BB, Dutta S, Dziedzic AM, Edinur HA, Ekholuenetale M, Ekundayo TC, Elgendy IY, Elhadi M, El-Huneidi W, Elmeligy OAA, Elmonem MA, Endeshaw D, Esayas HL, Eshetu HB, Etaee F, Fadhil I, Fagbamigbe AF, Fahim A, Falahi S, Faris MEM, Farrokhpour H, Farzadfar F, Fatehizadeh A, Fazli G, Feng X, Ferede TY, Fischer F, Flood D, Forouhari A, Foroumadi R, Foroutan Koudehi M, Gaidhane AM, Gaihre S, Gaipov A, Galali Y, Ganesan B, Garcia-Gordillo MA, Gautam RK, Gebrehiwot M, Gebrekidan KG, Gebremeskel TG, Getacher L, Ghadirian F, Ghamari SH, Ghasemi Nour M, Ghassemi F, Golechha M, Goleij P, Golinelli D, Gopalani SV, Guadie HA, Guan SY, Gudayu TW, Guimarães RA, Guled RA, Gupta R, Gupta K, Gupta VB, Gupta VK, Gyawali B, Haddadi R, Hadi NR, Haile TG, Hajibeygi R, Haj-Mirzaian A, Halwani R, Hamidi S, Hankey GJ, Hannan MA, Haque S, Harandi H, Harlianto NI, Hasan SMM, Hasan SS, Hasani H, Hassanipour S, Hassen MB, Haubold J, Hayat K, Heidari G, Heidari M, Hessami K, Hiraike Y, Holla R, Hossain S, Hossain MS, Hosseini MS, Hosseinzadeh M, Hosseinzadeh H, Huang J, Huda MN, Hussain S, Huynh HH, Hwang BF, Ibitoye SE, Ikeda N, Ilic IM, Ilic MD, Inbaraj LR, Iqbal A, Islam SMS, Islam RM, Ismail NE, Iso H, Isola G, Itumalla R, Iwagami M, Iwu CCD, Iyamu IO, Iyasu AN, Jacob L, Jafarzadeh A, Jahrami H, Jain R, Jaja C, Jamalpoor Z, Jamshidi E, Janakiraman B, Jayanna K, Jayapal SK, Jayaram S, Jayawardena R, Jebai R, Jeong W, Jin Y, Jokar M, Jonas JB, Joseph N, Joseph A, Joshua CE, Joukar F, Jozwiak JJ, Kaambwa B, Kabir A, Kabthymer RH, Kadashetti V, Kahe F, Kalhor R, Kandel H, Karanth SD, Karaye IM, Karkhah S, Katoto PDMC, Kaur N, Kazemian S, Kebede SA, Khader YS, Khajuria H, Khalaji A, Khan MAB, Khan M, Khan A, Khanal S, Khatatbeh MM, Khater AM, Khateri S, khorashadizadeh F, Khubchandani J, Kibret BG, Kim MS, Kimokoti RW, Kisa A, Kivimäki M, Kolahi AA, Komaki S, Kompani F, Koohestani HR, Korzh O, Kostev K, Kothari N, Koyanagi A, Krishan K, Krishnamoorthy Y, Kuate Defo B, Kuddus M, Kuddus MA, Kumar R, Kumar H, Kundu S, Kurniasari MD, Kuttikkattu A, La Vecchia C, Lallukka T, Larijani B, Larsson AO, Latief K, Lawal BK, Le TTT, Le TTB, Lee SWH, Lee M, Lee WC, Lee PH, Lee SW, Lee SW, Legesse SM, Lenzi J, Li Y, Li MC, Lim SS, Lim LL, Liu X, Liu C, Lo CH, Lopes G, Lorkowski S, Lozano R, Lucchetti G, Maghazachi AA, Mahasha PW, Mahjoub S, Mahmoud MA, Mahmoudi R, Mahmoudimanesh M, Mai AT, Majeed A, Majma Sanaye P, Makris KC, Malhotra K, Malik AA, Malik I, Mallhi TH, Malta DC, Mamun AA, Mansouri B, Marateb HR, Mardi P, Martini S, Martorell M, Marzo RR, Masoudi R, Masoudi S, Mathews E, Maugeri A, Mazzaglia G, Mekonnen T, Meshkat M, Mestrovic T, Miao Jonasson J, Miazgowski T, Michalek IM, Minh LHN, Mini GK, Miranda JJ, Mirfakhraie R, Mirrakhimov EM, Mirza-Aghazadeh-Attari M, Misganaw A, Misgina KH, Mishra M, Moazen B, Mohamed NS, Mohammadi E, Mohammadi M, Mohammadian-Hafshejani A, Mohammadshahi M, Mohseni A, Mojiri-forushani H, Mokdad AH, Momtazmanesh S, Monasta L, Moniruzzaman M, Mons U, Montazeri F, Moodi Ghalibaf A, Moradi Y, Moradi M, Moradi Sarabi M, Morovatdar N, Morrison SD, Morze J, Mossialos E, Mostafavi E, Mueller UO, Mulita F, Mulita A, Murillo-Zamora E, Musa KI, Mwita JC, Nagaraju SP, Naghavi M, Nainu F, Nair TS, Najmuldeen HHR, Nangia V, Nargus S, Naser AY, Nassereldine H, Natto ZS, Nauman J, Nayak BP, Ndejjo R, Negash H, Negoi RI, Nguyen HTH, Nguyen DH, Nguyen PT, Nguyen VT, Nguyen HQ, Niazi RK, Nigatu YT, Ningrum DNA, Nizam MA, Nnyanzi LA, Noreen M, Noubiap JJ, Nzoputam OJ, Nzoputam CI, Oancea B, Odogwu NM, Odukoya OO, Ojha VA, Okati-Aliabad H, Okekunle AP, Okonji OC, Okwute PG, Olufadewa II, Onwujekwe OE, Ordak M, Ortiz A, Osuagwu UL, Oulhaj A, Owolabi MO, Padron-Monedero A, Padubidri JR, Palladino R, Panagiotakos D, Panda-Jonas S, Pandey A, Pandey A, Pandi-Perumal SR, Pantea Stoian AM, Pardhan S, Parekh T, Parekh U, Pasovic M, Patel J, Patel JR, Paudel U, Pepito VCF, Pereira M, Perico N, Perna S, Petcu IR, Petermann-Rocha FE, Podder V, Postma MJ, Pourali G, Pourtaheri N, Prates EJS, Qadir MMF, Qattea I, Raee P, Rafique I, Rahimi M, Rahimifard M, Rahimi-Movaghar V, Rahman MO, Rahman MA, Rahman MHU, Rahman M, Rahman MM, Rahmani M, Rahmani S, Rahmanian V, Rahmawaty S, Rahnavard N, Rajbhandari B, Ram P, Ramazanu S, Rana J, Rancic N, Ranjha MMAN, Rao CR, Rapaka D, Rasali DP, Rashedi S, Rashedi V, Rashid AM, Rashidi MM, Ratan ZA, Rawaf S, Rawal L, Redwan EMM, Remuzzi G, Rengasamy KRR, Renzaho AMN, Reyes LF, Rezaei N, Rezaei N, Rezaeian M, Rezazadeh H, Riahi SM, Rias YA, Riaz M, Ribeiro D, Rodrigues M, Rodriguez JAB, Roever L, Rohloff P, Roshandel G, Roustazadeh A, Rwegerera GM, Saad AMA, Saber-Ayad MM, Sabour S, Sabzmakan L, Saddik B, Sadeghi E, Saeed U, Saeedi Moghaddam S, Safi S, Safi SZ, Saghazadeh A, Saheb Sharif-Askari N, Saheb Sharif-Askari F, Sahebkar A, Sahoo SS, Sahoo H, Saif-Ur-Rahman KM, Sajid MR, Salahi S, Salahi S, Saleh MA, Salehi MA, Salomon JA, Sanabria J, Sanjeev RK, Sanmarchi F, Santric-Milicevic MM, Sarasmita MA, Sargazi S, Sathian B, Sathish T, Sawhney M, Schlaich MP, Schmidt MI, Schuermans A, Seidu AA, Senthil Kumar N, Sepanlou SG, Sethi Y, Seylani A, Shabany M, Shafaghat T, Shafeghat M, Shafie M, Shah NS, Shahid S, Shaikh MA, Shanawaz M, Shannawaz M, Sharfaei S, Shashamo BB, Shiri R, Shittu A, Shivakumar KM, Shivalli S, Shobeiri P, Shokri F, Shuval K, Sibhat MM, Silva LMLR, Simpson CR, Singh JA, Singh P, Singh S, Siraj MS, Skryabina AA, Sohag AAM, Soleimani H, Solikhah S, Soltani-Zangbar MS, Somayaji R, Sorensen RJD, Starodubova AV, Sujata S, Suleman M, Sun J, Sundström J, Tabarés-Seisdedos R, Tabatabaei SM, Tabatabaeizadeh SA, Tabish M, Taheri M, Taheri E, Taki E, Tamuzi JJLL, Tan KK, Tat NY, Taye BT, Temesgen WA, Temsah MH, Tesler R, Thangaraju P, Thankappan KR, Thapa R, Tharwat S, Thomas N, Ticoalu JHV, Tiyuri A, Tonelli M, Tovani-Palone MR, Trico D, Trihandini I, Tripathy JP, Tromans SJ, Tsegay GM, Tualeka AR, Tufa DG, Tyrovolas S, Ullah S, Upadhyay E, Vahabi SM, Vaithinathan AG, Valizadeh R, van Daalen KR, Vart P, Varthya SB, Vasankari TJ, Vaziri S, Verma MV, Verras GI, Vo DC, Wagaye B, Waheed Y, Wang Z, Wang Y, Wang C, Wang F, Wassie GT, Wei MYW, Weldemariam AH, Westerman R, Wickramasinghe ND, Wu Y, Wulandari RDWI, Xia J, Xiao H, Xu S, Xu X, Yada DY, Yang L, Yatsuya H, Yesiltepe M, Yi S, Yohannis HK, Yonemoto N, You Y, Zaman SB, Zamora N, Zare I, Zarea K, Zarrintan A, Zastrozhin MS, Zeru NG, Zhang ZJ, Zhong C, Zhou J, Zielińska M, Zikarg YT, Zodpey S, Zoladl M, Zou Z, Zumla A, Zuniga YMH, Magliano DJ, Murray CJL, Hay SI, Vos T. Global, regional, and national burden of diabetes from 1990 to 2021, with projections of prevalence to 2050: a systematic analysis for the Global Burden of Disease Study 2021. Lancet 2023; 402:203-234. [PMID: 37356446 PMCID: PMC10364581 DOI: 10.1016/s0140-6736(23)01301-6] [Citation(s) in RCA: 250] [Impact Index Per Article: 250.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 06/27/2023]
Abstract
BACKGROUND Diabetes is one of the leading causes of death and disability worldwide, and affects people regardless of country, age group, or sex. Using the most recent evidentiary and analytical framework from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD), we produced location-specific, age-specific, and sex-specific estimates of diabetes prevalence and burden from 1990 to 2021, the proportion of type 1 and type 2 diabetes in 2021, the proportion of the type 2 diabetes burden attributable to selected risk factors, and projections of diabetes prevalence through 2050. METHODS Estimates of diabetes prevalence and burden were computed in 204 countries and territories, across 25 age groups, for males and females separately and combined; these estimates comprised lost years of healthy life, measured in disability-adjusted life-years (DALYs; defined as the sum of years of life lost [YLLs] and years lived with disability [YLDs]). We used the Cause of Death Ensemble model (CODEm) approach to estimate deaths due to diabetes, incorporating 25 666 location-years of data from vital registration and verbal autopsy reports in separate total (including both type 1 and type 2 diabetes) and type-specific models. Other forms of diabetes, including gestational and monogenic diabetes, were not explicitly modelled. Total and type 1 diabetes prevalence was estimated by use of a Bayesian meta-regression modelling tool, DisMod-MR 2.1, to analyse 1527 location-years of data from the scientific literature, survey microdata, and insurance claims; type 2 diabetes estimates were computed by subtracting type 1 diabetes from total estimates. Mortality and prevalence estimates, along with standard life expectancy and disability weights, were used to calculate YLLs, YLDs, and DALYs. When appropriate, we extrapolated estimates to a hypothetical population with a standardised age structure to allow comparison in populations with different age structures. We used the comparative risk assessment framework to estimate the risk-attributable type 2 diabetes burden for 16 risk factors falling under risk categories including environmental and occupational factors, tobacco use, high alcohol use, high body-mass index (BMI), dietary factors, and low physical activity. Using a regression framework, we forecast type 1 and type 2 diabetes prevalence through 2050 with Socio-demographic Index (SDI) and high BMI as predictors, respectively. FINDINGS In 2021, there were 529 million (95% uncertainty interval [UI] 500-564) people living with diabetes worldwide, and the global age-standardised total diabetes prevalence was 6·1% (5·8-6·5). At the super-region level, the highest age-standardised rates were observed in north Africa and the Middle East (9·3% [8·7-9·9]) and, at the regional level, in Oceania (12·3% [11·5-13·0]). Nationally, Qatar had the world's highest age-specific prevalence of diabetes, at 76·1% (73·1-79·5) in individuals aged 75-79 years. Total diabetes prevalence-especially among older adults-primarily reflects type 2 diabetes, which in 2021 accounted for 96·0% (95·1-96·8) of diabetes cases and 95·4% (94·9-95·9) of diabetes DALYs worldwide. In 2021, 52·2% (25·5-71·8) of global type 2 diabetes DALYs were attributable to high BMI. The contribution of high BMI to type 2 diabetes DALYs rose by 24·3% (18·5-30·4) worldwide between 1990 and 2021. By 2050, more than 1·31 billion (1·22-1·39) people are projected to have diabetes, with expected age-standardised total diabetes prevalence rates greater than 10% in two super-regions: 16·8% (16·1-17·6) in north Africa and the Middle East and 11·3% (10·8-11·9) in Latin America and Caribbean. By 2050, 89 (43·6%) of 204 countries and territories will have an age-standardised rate greater than 10%. INTERPRETATION Diabetes remains a substantial public health issue. Type 2 diabetes, which makes up the bulk of diabetes cases, is largely preventable and, in some cases, potentially reversible if identified and managed early in the disease course. However, all evidence indicates that diabetes prevalence is increasing worldwide, primarily due to a rise in obesity caused by multiple factors. Preventing and controlling type 2 diabetes remains an ongoing challenge. It is essential to better understand disparities in risk factor profiles and diabetes burden across populations, to inform strategies to successfully control diabetes risk factors within the context of multiple and complex drivers. FUNDING Bill & Melinda Gates Foundation.
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Haubold J, Jost G, Theysohn JM, Ludwig JM, Li Y, Kleesiek J, Schaarschmidt BM, Forsting M, Nensa F, Pietsch H, Hosch R. Contrast Agent Dose Reduction in MRI Utilizing a Generative Adversarial Network in an Exploratory Animal Study. Invest Radiol 2023; 58:396-404. [PMID: 36728299 DOI: 10.1097/rli.0000000000000947] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Abstract
OBJECTIVES The aim of this study is to use virtual contrast enhancement to reduce the amount of hepatobiliary gadolinium-based contrast agent in magnetic resonance imaging with generative adversarial networks (GANs) in a large animal model. METHODS With 20 healthy Göttingen minipigs, a total of 120 magnetic resonance imaging examinations were performed on 6 different occasions, 50% with reduced (low-dose; 0.005 mmol/kg, gadoxetate) and 50% standard dose (normal-dose; 0.025 mmol/kg). These included arterial, portal venous, venous, and hepatobiliary contrast phases (20 minutes, 30 minutes). Because of incomplete examinations, one animal had to be excluded. Randomly, 3 of 19 animals were selected and withheld for validation (18 examinations). Subsequently, a GAN was trained for image-to-image conversion from low-dose to normal-dose (virtual normal-dose) with the remaining 16 animals (96 examinations). For validation, vascular and parenchymal contrast-to-noise ratio (CNR) was calculated using region of interest measurements of the abdominal aorta, inferior vena cava, portal vein, hepatic parenchyma, and autochthonous back muscles. In parallel, a visual Turing test was performed by presenting the normal-dose and virtual normal-dose data to 3 consultant radiologists, blinded for the type of examination. They had to decide whether they would consider both data sets as consistent in findings and which images were from the normal-dose study. RESULTS The pooled dynamic phase vascular and parenchymal CNR increased significantly from low-dose to virtual normal-dose (pooled vascular: P < 0.0001, pooled parenchymal: P = 0.0002) and was found to be not significantly different between virtual normal-dose and normal-dose examinations (vascular CNR [mean ± SD]: low-dose 17.6 ± 6.0, virtual normal-dose 41.8 ± 9.7, and normal-dose 48.4 ± 12.2; parenchymal CNR [mean ± SD]: low-dose 20.2 ± 5.9, virtual normal-dose 28.3 ± 6.9, and normal-dose 29.5 ± 7.2). The pooled parenchymal CNR of the hepatobiliary contrast phases revealed a significant increase from the low-dose (22.8 ± 6.2) to the virtual normal-dose (33.2 ± 6.1; P < 0.0001) and normal-dose sequence (37.0 ± 9.1; P < 0.0001). In addition, there was no significant difference between the virtual normal-dose and normal-dose sequence. In the visual Turing test, on the median, the consultant radiologist reported that the sequences of the normal-dose and virtual normal-dose are consistent in findings in 100% of the examinations. Moreover, the consultants were able to identify the normal-dose series as such in a median 54.5% of the cases. CONCLUSIONS In this feasibility study in healthy Göttingen minipigs, it could be shown that GAN-based virtual contrast enhancement can be used to recreate the image impression of normal-dose imaging in terms of CNR and subjective image similarity in both dynamic and hepatobiliary contrast phases from low-dose data with an 80% reduction in gadolinium-based contrast agent dose. Before clinical implementation, further studies with pathologies are needed to validate whether pathologies are correctly represented by the network.
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Affiliation(s)
| | - Gregor Jost
- MR and CT Contrast Media Research, Bayer AG, Berlin, Germany
| | - Jens Matthias Theysohn
- From the Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen
| | - Johannes Maximilian Ludwig
- From the Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen
| | - Yan Li
- From the Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen
| | - Jens Kleesiek
- Institute of Artificial Intelligence in Medicine, University Hospital Essen, Essen
| | | | - Michael Forsting
- From the Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen
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Haubold J, Zensen S, Hosch R, Schaarschmidt BM, Bos D, Schmidt B, Flohr T, Li Y, Forsting M, Pietsch H, Nensa F, Jost G. Individualized scan protocols for CT angiography: an animal study for contrast media or radiation dose optimization. Eur Radiol Exp 2023; 7:24. [PMID: 37185930 PMCID: PMC10130261 DOI: 10.1186/s41747-023-00332-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2022] [Accepted: 02/16/2023] [Indexed: 05/17/2023] Open
Abstract
BACKGROUND We investigated about optimization of contrast media (CM) dose or radiation dose in thoracoabdominal computed tomography angiography (CTA) by automated tube voltage selection (ATVS) system configuration and CM protocol adaption. METHODS In six minipigs, CTA-optimized protocols were evaluated regarding objective (contrast-to-noise ratio, CNR) and subjective (6 criteria assessed by Likert scale) image quality. Scan parameters were automatically adapted by the ATVS system operating at 90-kV semi-mode and configured for standard, CM saving, or radiation dose saving (image task, quality settings). Injection protocols (dose, flow rate) were adapted manually. This approach was tested for normal and simulated obese conditions. RESULTS Radiation exposure (volume-weighted CT dose index) for normal (obese) conditions was 2.4 ± 0.7 (5.0 ± 0.7) mGy (standard), 4.3 ± 1.1 (9.0 ± 1.3) mGy (CM reduced), and 1.7 ± 0.5 (3.5 ± 0.5) mGy (radiation reduced). The respective CM doses for normal (obese) settings were 210 (240) mgI/kg, 155 (177) mgI/kg, and 252 (288) mgI/kg. No significant differences in CNR (normal; obese) were observed between standard (17.8 ± 3.0; 19.2 ± 4.0), CM-reduced (18.2 ± 3.3; 20.5 ± 4.9), and radiation-saving CTAs (16.0 ± 3.4; 18.4 ± 4.1). Subjective analysis showed similar values for optimized and standard CTAs. Only the parameter diagnostic acceptability was significantly lower for radiation-saving CTA compared to the standard CTA. CONCLUSIONS The CM dose (-26%) or radiation dose (-30%) for thoracoabdominal CTA can be reduced while maintaining objective and subjective image quality, demonstrating the feasibility of the personalization of CTA scan protocols. KEY POINTS • Computed tomography angiography protocols could be adapted to individual patient requirements using an automated tube voltage selection system combined with adjusted contrast media injection. • Using an adapted automated tube voltage selection system, a contrast media dose reduction (-26%) or radiation dose reduction (-30%) could be possible.
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Affiliation(s)
- Johannes Haubold
- Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, 45147, Essen, Germany.
| | - Sebastian Zensen
- Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, 45147, Essen, Germany
| | - René Hosch
- Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, 45147, Essen, Germany
- Institute of Artificial Intelligence in Medicine, University Hospital Essen, Essen, Germany
| | - Benedikt Michael Schaarschmidt
- Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, 45147, Essen, Germany
| | - Denise Bos
- Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, 45147, Essen, Germany
| | | | | | - Yan Li
- Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, 45147, Essen, Germany
| | - Michael Forsting
- Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, 45147, Essen, Germany
| | | | - Felix Nensa
- Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, 45147, Essen, Germany
- Institute of Artificial Intelligence in Medicine, University Hospital Essen, Essen, Germany
| | - Gregor Jost
- MR and CT Contrast Media Research, Bayer AG, Berlin, Germany
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Alatzides GL, Haubold J, Steinberg HL, Koitka S, Parmar V, Grueneisen J, Zeller AC, Schmidt H, Theysohn JM, Li Y, Nensa F, Schaarschmidt BM. Adipopenia in body composition analysis: a promising imaging biomarker and potential predictive factor for patients undergoing transjugular intrahepatic portosystemic shunt placement. Br J Radiol 2023; 96:20220863. [PMID: 37086078 DOI: 10.1259/bjr.20220863] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/23/2023] Open
Abstract
OBJECTIVE Body tissue composition plays a crucial role in the multisystemic processes of advanced liver disease and has been shown to be influenced by transjugular intrahepatic portosystemic shunt (TIPS). A differentiated analysis of the various tissue compartments has not been performed until now. The purpose of this study was to evaluate the value of imaging biomarkers derived from automated body composition analysis (BCA) to predict clinical and functional outcome. METHODS A retrospective analysis of 56 patients undergoing TIPS procedure between 2013 and 2021 was performed. BCA on the base of pre-interventional CT examination was used to determine quantitative data as well as ratios of bone, muscle and fat masses. Furthermore, a BCA-derived sarcopenia marker was investigated. Regarding potential correlations between BCA imaging biomarkers and the occurrence of hepatic encephalopathy (HE) as well as 1-year survival, an exploratory analysis was conducted. RESULTS No BCA imaging biomarker was associated with the occurrence of HE after TIPS placement. However, there were significant differences in alive and deceased patients regarding the BCA-derived sarcopenia marker (alive: 1.60, deceased: 1.83, p = 0.046), ratios of intra- and intermuscular fat/skeletal volume (alive: 0.53, deceased: 0.31, p = 0.015) and intra- and intermuscular fat/muscle volume (alive: 0.21, deceased: 0.14, p = 0.031). CONCLUSION A lower amount of intra- and intermuscular adipose tissue might have protective effects regarding liver derived complications and survival. ADVANCES IN KNOWLEDGE Precise characterization of body tissue components with automated BCA might provide prognostic information in patients with advanced liver disease undergoing TIPS procedure.
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Affiliation(s)
- Georgios Luca Alatzides
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Hufelandstr, Essen, Germany
| | - Johannes Haubold
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Hufelandstr, Essen, Germany
- Institute for Artificial Intelligence in Medicine (IKIM), University Hospital Essen, Essen, Hufelandstr, Germany
| | - Hannah Luisa Steinberg
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Hufelandstr, Essen, Germany
| | - Sven Koitka
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Hufelandstr, Essen, Germany
- Institute for Artificial Intelligence in Medicine (IKIM), University Hospital Essen, Essen, Hufelandstr, Germany
| | - Vicky Parmar
- Institute for Artificial Intelligence in Medicine (IKIM), University Hospital Essen, Essen, Hufelandstr, Germany
| | - Johannes Grueneisen
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Hufelandstr, Essen, Germany
| | - Amos Cornelius Zeller
- Department of Gastroenterology and Hepatology, University Hospital Essen, Hufelandstr, Essen, Germany
| | - Hartmut Schmidt
- Department of Gastroenterology and Hepatology, University Hospital Essen, Hufelandstr, Essen, Germany
| | - Jens Matthias Theysohn
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Hufelandstr, Essen, Germany
| | - Yan Li
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Hufelandstr, Essen, Germany
| | - Felix Nensa
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Hufelandstr, Essen, Germany
- Institute for Artificial Intelligence in Medicine (IKIM), University Hospital Essen, Essen, Hufelandstr, Germany
| | - Benedikt Michael Schaarschmidt
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Hufelandstr, Essen, Germany
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Haubold J, Zeng K, Farhand S, Stalke S, Steinberg H, Bos D, Meetschen M, Kureishi A, Zensen S, Goeser T, Maier S, Forsting M, Nensa F. AI co-pilot: content-based image retrieval for the reading of rare diseases in chest CT. Sci Rep 2023; 13:4336. [PMID: 36928759 PMCID: PMC10020154 DOI: 10.1038/s41598-023-29949-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2022] [Accepted: 02/13/2023] [Indexed: 03/18/2023] Open
Abstract
The aim of the study was to evaluate the impact of the newly developed Similar patient search (SPS) Web Service, which supports reading complex lung diseases in computed tomography (CT), on the diagnostic accuracy of residents. SPS is an image-based search engine for pre-diagnosed cases along with related clinical reference content ( https://eref.thieme.de ). The reference database was constructed using 13,658 annotated regions of interest (ROIs) from 621 patients, comprising 69 lung diseases. For validation, 50 CT scans were evaluated by five radiology residents without SPS, and three months later with SPS. The residents could give a maximum of three diagnoses per case. A maximum of 3 points was achieved if the correct diagnosis without any additional diagnoses was provided. The residents achieved an average score of 17.6 ± 5.0 points without SPS. By using SPS, the residents increased their score by 81.8% to 32.0 ± 9.5 points. The improvement of the score per case was highly significant (p = 0.0001). The residents required an average of 205.9 ± 350.6 s per case (21.9% increase) when SPS was used. However, in the second half of the cases, after the residents became more familiar with SPS, this increase dropped to 7%. Residents' average score in reading complex chest CT scans improved by 81.8% when the AI-driven SPS with integrated clinical reference content was used. The increase in time per case due to the use of the SPS was minimal.
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Affiliation(s)
- Johannes Haubold
- Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Hufelandstraße 55, 45147, Essen, Germany.
- Institute of Artificial Intelligence in Medicine, University Hospital Essen, Hufelandstraße 55, 45147, Essen, Germany.
| | - Ke Zeng
- Siemens Medical Solutions Inc., Malvern, PA, USA
| | | | | | - Hannah Steinberg
- Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Hufelandstraße 55, 45147, Essen, Germany
| | - Denise Bos
- Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Hufelandstraße 55, 45147, Essen, Germany
| | - Mathias Meetschen
- Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Hufelandstraße 55, 45147, Essen, Germany
| | - Anisa Kureishi
- Institute of Artificial Intelligence in Medicine, University Hospital Essen, Hufelandstraße 55, 45147, Essen, Germany
| | - Sebastian Zensen
- Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Hufelandstraße 55, 45147, Essen, Germany
| | - Tim Goeser
- Department of Radiology and Neuroradiology, Kliniken Maria Hilf, Viersener Str. 450, 41063, Mönchengladbach, NRW, Germany
| | - Sandra Maier
- Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Hufelandstraße 55, 45147, Essen, Germany
| | - Michael Forsting
- Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Hufelandstraße 55, 45147, Essen, Germany
| | - Felix Nensa
- Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Hufelandstraße 55, 45147, Essen, Germany
- Institute of Artificial Intelligence in Medicine, University Hospital Essen, Hufelandstraße 55, 45147, Essen, Germany
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Saner YM, Wiesenfarth M, Weru V, Ladyzhensky B, Tschirdewahn S, Püllen L, Bonekamp D, Reis H, Krafft U, Heß J, Kesch C, Darr C, Forsting M, Wetter A, Umutlu L, Haubold J, Hadaschik B, Radtke JP. Detection of Clinically Significant Prostate Cancer Using Targeted Biopsy with Four Cores Versus Target Saturation Biopsy with Nine Cores in Transperineal Prostate Fusion Biopsy: A Prospective Randomized Trial. Eur Urol Oncol 2023; 6:49-55. [PMID: 36175281 DOI: 10.1016/j.euo.2022.08.005] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2021] [Revised: 08/04/2022] [Accepted: 08/29/2022] [Indexed: 11/26/2022]
Abstract
BACKGROUND Multiparametric magnetic resonance imaging (mpMRI) and targeted biopsy (TB) facilitate accurate detection of clinically significant prostate cancer (csPC). However, it remains unclear how targeted cores should be applied for accurate diagnosis of csPC. OBJECTIVE To assess csPC detection rates for two target-directed MRI/transrectal ultrasonography (TRUS) fusion biopsy approaches, conventional TB and target saturation biopsy (TS). DESIGN, SETTING, AND PARTICIPANTS This was a prospective single-center study of outcomes for transperineal MRI/TRUS fusion biopsies for 170 men. Half of the men (n = 85) were randomized to conventional TB with four cores per lesion and half (n = 85) to TS with nine cores. Biopsies were performed by three experienced board-certified urologists. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS PC and csPC (International Society of Urological Pathology grade group ≥2) detection rates for systematic biopsy (SB), TB, and TS were analyzed using McNemar's test for intrapatient comparisons and Fisher's exact test for TS versus TB. A combination of targeted biopsy (TS or TB) and SB served as the reference. RESULTS AND LIMITATIONS According to the reference, csPC was diagnosed for 57 men in the TS group and 36 men in the TB group. Of these, TS detected 57/57 csPC cases and TB detected 33/36 csPC cases (p = 0.058). Detection of Gleason grade group 1 disease was 10/12 cases with TS and 8/17 cases with TB (p = 0.055). In addition, TS detected 97% of 63 csPC lesions, compared to 86% with TB (p = 0.1). Limitations include the single-center design, the limited generalizability owing to the transperineal biopsy route, the lack of central review of pathology and radical prostatectomy correlation, and uneven distributions of csPC prevalence, Prostate Imaging-Reporting and Data System (PI-RADS) 5 lesions, men with two or more PI-RADS ≥3 lesions, and prostate-specific antigen density between the groups, which may have affected the results. CONCLUSIONS In our study, rates of csPC detection did not significantly differ between TS and TB. PATIENT SUMMARY In this study, we investigated two targeted approaches for taking prostate biopsy samples after observation of suspicious lesions on prostate scans. We found that the rates of detection of prostate cancer did not significantly differ between the two approaches.
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Affiliation(s)
| | - Manuel Wiesenfarth
- Division of Biostatistics, German Cancer Research Center, Heidelberg, Germany
| | - Vivienn Weru
- Division of Biostatistics, German Cancer Research Center, Heidelberg, Germany
| | - Boris Ladyzhensky
- Department of Anesthesia and Perioperative Medicine, University Hospitals Cleveland Medical Center, Cleveland, OH, USA
| | | | - Lukas Püllen
- Department of Urology, University Hospital Essen, Essen, Germany
| | - David Bonekamp
- Department of Radiology, German Cancer Research Center, Heidelberg, Germany
| | - Henning Reis
- Institute of Pathology, University Duisburg-Essen, Essen, Germany
| | - Ulrich Krafft
- Department of Urology, University Hospital Essen, Essen, Germany
| | - Jochen Heß
- Department of Urology, University Hospital Essen, Essen, Germany
| | - Claudia Kesch
- Department of Urology, University Hospital Essen, Essen, Germany
| | - Christopher Darr
- Department of Urology, University Hospital Essen, Essen, Germany
| | - Michael Forsting
- Institute of Diagnostic and Interventional Radiology, University Hospital Essen, Essen, Germany
| | - Axel Wetter
- Institute of Diagnostic and Interventional Radiology, University Hospital Essen, Essen, Germany
| | - Lale Umutlu
- Institute of Diagnostic and Interventional Radiology, University Hospital Essen, Essen, Germany
| | - Johannes Haubold
- Institute of Diagnostic and Interventional Radiology, University Hospital Essen, Essen, Germany
| | - Boris Hadaschik
- Department of Urology, University Hospital Essen, Essen, Germany
| | - Jan Philipp Radtke
- Department of Urology, University Hospital Essen, Essen, Germany; Department of Radiology, German Cancer Research Center, Heidelberg, Germany.
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Müller L, Mähringer-Kunz A, Auer TA, Fehrenbach U, Gebauer B, Haubold J, Theysohn JM, Kim MS, Kleesiek J, Diallo TD, Eisenblätter M, Bettinger D, Steinle V, Mayer P, Zopfs D, Pinto Dos Santos D, Kloeckner R. Low bone mineral density is a prognostic factor for elderly patients with HCC undergoing TACE: results from a multicenter study. Eur Radiol 2023; 33:1031-1039. [PMID: 35986768 PMCID: PMC9889510 DOI: 10.1007/s00330-022-09069-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2022] [Revised: 06/30/2022] [Accepted: 07/24/2022] [Indexed: 02/04/2023]
Abstract
OBJECTIVES Low bone mineral density (BMD) was recently identified as a novel risk factor for patients with hepatocellular carcinoma (HCC). In this multicenter study, we aimed to validate the role of BMD as a prognostic factor for patients with HCC undergoing transarterial chemoembolization (TACE). METHODS This retrospective multicenter trial included 908 treatment-naïve patients with HCC who were undergoing TACE as a first-line treatment, at six tertiary care centers, between 2010 and 2020. BMD was assessed by measuring the mean Hounsfield units (HUs) in the midvertebral core of the 11th thoracic vertebra, on contrast-enhanced computer tomography performed before treatment. We assessed the influence of BMD on median overall survival (OS) and performed multivariate analysis including established estimates for survival. RESULTS The median BMD was 145 HU (IQR, 115-175 HU). Patients with a high BMD (≥ 114 HU) had a median OS of 22.2 months, while patients with a low BMD (< 114 HU) had a lower median OS of only 16.2 months (p < .001). Besides albumin, bilirubin, tumor number, and tumor diameter, BMD remained an independent prognostic factor in multivariate analysis. CONCLUSIONS BMD is an independent predictive factor for survival in elderly patients with HCC undergoing TACE. The integration of BMD into novel scoring systems could potentially improve survival prediction and clinical decision-making. KEY POINTS • Bone mineral density can be easily assessed in routinely acquired pre-interventional computed tomography scans. • Bone mineral density is an independent predictive factor for survival in elderly patients with HCC undergoing TACE. • Thus, bone mineral density is a novel imaging biomarker for prognosis prediction in elderly patients with HCC undergoing TACE.
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Affiliation(s)
- Lukas Müller
- Department of Diagnostic and Interventional Radiology, University Medical Center of the Johannes Gutenberg University Mainz, Langenbeckst. 1, 55131, Mainz, Germany
| | - Aline Mähringer-Kunz
- Department of Diagnostic and Interventional Radiology, University Medical Center of the Johannes Gutenberg University Mainz, Langenbeckst. 1, 55131, Mainz, Germany
| | - Timo Alexander Auer
- Department of Radiology, Charité - University Medicine Berlin, Berlin, Germany
| | - Uli Fehrenbach
- Department of Radiology, Charité - University Medicine Berlin, Berlin, Germany
| | - Bernhard Gebauer
- Department of Radiology, Charité - University Medicine Berlin, Berlin, Germany
| | - Johannes Haubold
- Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany
| | - Jens M Theysohn
- Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany
| | - Moon-Sung Kim
- Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany
| | - Jens Kleesiek
- Institute for AI in Medicine (IKIM), University Hospital Essen, Essen, Germany
| | - Thierno D Diallo
- Department of Diagnostic and Interventional Radiology, Freiburg University Hospital, Freiburg, Germany
| | - Michel Eisenblätter
- Department of Diagnostic and Interventional Radiology, Freiburg University Hospital, Freiburg, Germany
| | - Dominik Bettinger
- Department of Medicine II, Medical Center University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Verena Steinle
- Department of Diagnostic and Interventional Radiology, University Medical Center Heidelberg, Heidelberg, Germany
| | - Philipp Mayer
- Department of Diagnostic and Interventional Radiology, University Medical Center Heidelberg, Heidelberg, Germany
| | - David Zopfs
- Department of Radiology, University Hospital Cologne, Cologne, Germany
| | | | - Roman Kloeckner
- Department of Diagnostic and Interventional Radiology, University Medical Center of the Johannes Gutenberg University Mainz, Langenbeckst. 1, 55131, Mainz, Germany.
- Department for Interventional Radiology, University Hospital of Lübeck, Ratzeburger Allee 160, Lübeck, Germany.
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Beck S, Dittrich F, Busch A, Jäger M, Theysohn JM, Lazik-Palm A, Haubold J. Unloader bracing in osteoarthritis of the knee - Is there a direct effect on the damaged cartilage? Knee 2023; 40:16-23. [PMID: 36403395 DOI: 10.1016/j.knee.2022.11.003] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/13/2021] [Revised: 08/26/2022] [Accepted: 11/03/2022] [Indexed: 11/18/2022]
Abstract
BACKGROUND Unloading knee braces represent a conservative treatment option for non-pharmalogical management of unicompartmental osteoarthritis of the knee. Though there is consensus on the clinical effectiveness of unloading, the effect mechanism of bracing remains part of a debate. Our study was designed to assess the effect of unloader bracing on damaged cartilage via MRI cartilage mappings. METHODS Fourteen patients (7 female, 7 male, mean age 43.1 ± 9.4 years) with unicompartmental cartilage wear in knees with varus or valgus malalignment were enrolled. Clinical scores, radiographs and MR-graphic properties (T2/T2* mapping, T1 Delayed Gadolinium Enhanced MRI of the cartilage (dGEMRIC) mapping, high-resolution PDw sequences) of knee cartilage were recorded before and three months after brace use. RESULTS Bracing the knees for a mean of 14.4 ± 2.0 weeks (range 11 to 18 weeks) resulted in significant pain reduction (VAS changed from 5.9 ± 2.0 to 2.0 ± 1.3, p < 0.001) and improvement in knee function (KOOS increased from 42.1 ± 22.7 to 64.8 ± 18.7, p < 0.001). In the affected cartilage regions T2 relaxation times significantly decreased from 56.1 ± 11.4 ms to 46.5 ± 11.2 ms (p < 0.05). No changes in T1-dGEMRIC and T2* relaxation times, thickness or the extent of the damaged cartilage area could be detected. CONCLUSIONS Our results suggest, that unloader bracing improves the biochemical properties of the damaged cartilage by increasing collagen and proteoglycan concentration as well as decreasing the cartilage edema.
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Affiliation(s)
- S Beck
- Sportsclinic Hellersen, Paulmannshoeher Strasse 17, 58515 Luedenscheid, Germany; Department of Orthopedics and Trauma Surgery, University Hospital Essen, University of Duisburg-Essen, Hufelandstrasse 55, 45147 Essen, Germany.
| | - F Dittrich
- Department of Orthopedics and Trauma Surgery, University Hospital Essen, University of Duisburg-Essen, Hufelandstrasse 55, 45147 Essen, Germany; Gelenkzentrum Bergisch Land, Freiheitsstrasse 203, 42853 Remscheid, Germany
| | - A Busch
- Department of Orthopedics and Trauma Surgery, University Hospital Essen, University of Duisburg-Essen, Hufelandstrasse 55, 45147 Essen, Germany; Department of Orthopedics, Trauma and Reconstructive Surgery, St. Marien Hospital Muelheim, Contilia Gruppe, Kaiserstrasse 50, 45468 Muelheim an der Ruhr, Germany
| | - M Jäger
- Department of Orthopedics, Trauma and Reconstructive Surgery, St. Marien Hospital Muelheim, Contilia Gruppe, Kaiserstrasse 50, 45468 Muelheim an der Ruhr, Germany; Chair of Orthopedics and Trauma Surgery, University of Duisburg-Essen, Essen, Germany
| | - J M Theysohn
- Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Hufelandstrasse 55, 45147 Essen, Germany
| | - A Lazik-Palm
- Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Hufelandstrasse 55, 45147 Essen, Germany
| | - J Haubold
- Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Hufelandstrasse 55, 45147 Essen, Germany.
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35
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Koitka S, Gudlin P, Theysohn JM, Oezcelik A, Hoyer DP, Dayangac M, Hosch R, Haubold J, Flaschel N, Nensa F, Malamutmann E. Fully automated preoperative liver volumetry incorporating the anatomical location of the central hepatic vein. Sci Rep 2022; 12:16479. [PMID: 36183002 PMCID: PMC9526715 DOI: 10.1038/s41598-022-20778-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Accepted: 09/19/2022] [Indexed: 11/12/2022] Open
Abstract
The precise preoperative calculation of functional liver volumes is essential prior major liver resections, as well as for the evaluation of a suitable donor for living donor liver transplantation. The aim of this study was to develop a fully automated, reproducible, and quantitative 3D volumetry of the liver from standard CT examinations of the abdomen as part of routine clinical imaging. Therefore, an in-house dataset of 100 venous phase CT examinations for training and 30 venous phase ex-house CT examinations with a slice thickness of 5 mm for testing and validating were fully annotated with right and left liver lobe. Multi-Resolution U-Net 3D neural networks were employed for segmenting these liver regions. The Sørensen-Dice coefficient was greater than 0.9726 ± 0.0058, 0.9639 ± 0.0088, and 0.9223 ± 0.0187 and a mean volume difference of 32.12 ± 19.40 ml, 22.68 ± 21.67 ml, and 9.44 ± 27.08 ml compared to the standard of reference (SoR) liver, right lobe, and left lobe annotation was achieved. Our results show that fully automated 3D volumetry of the liver on routine CT imaging can provide reproducible, quantitative, fast and accurate results without needing any examiner in the preoperative work-up for hepatobiliary surgery and especially for living donor liver transplantation.
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Affiliation(s)
- Sven Koitka
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany.,Institute of Artificial Intelligence in Medicine, University Hospital Essen, Essen, Germany
| | - Phillip Gudlin
- Department of General, Visceral and Transplantation Surgery, University Hospital Essen, Essen, Germany
| | - Jens M Theysohn
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany
| | - Arzu Oezcelik
- Department of General, Visceral and Transplantation Surgery, University Hospital Essen, Essen, Germany
| | - Dieter P Hoyer
- Department of General, Visceral and Transplantation Surgery, University Hospital Essen, Essen, Germany
| | - Murat Dayangac
- Department of Surgery, Medipol University Hospital, Istanbul, Turkey
| | - René Hosch
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany.,Institute of Artificial Intelligence in Medicine, University Hospital Essen, Essen, Germany
| | - Johannes Haubold
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany
| | - Nils Flaschel
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany.,Institute of Artificial Intelligence in Medicine, University Hospital Essen, Essen, Germany
| | - Felix Nensa
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany. .,Institute of Artificial Intelligence in Medicine, University Hospital Essen, Essen, Germany.
| | - Eugen Malamutmann
- Department of General, Visceral and Transplantation Surgery, University Hospital Essen, Essen, Germany
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Haubold J, Jost G, Theysohn JM, Ludwig JM, Li Y, Kleesiek J, Schaarschmidt BM, Forsting M, Nensa F, Pietsch H, Hosch R. Contrast Media Reduction in Computed Tomography With Deep Learning Using a Generative Adversarial Network in an Experimental Animal Study. Invest Radiol 2022; 57:696-703. [PMID: 35438659 DOI: 10.1097/rli.0000000000000875] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Abstract
OBJECTIVE This feasibility study aimed to use optimized virtual contrast enhancement through generative adversarial networks (GAN) to reduce the dose of iodine-based contrast medium (CM) during abdominal computed tomography (CT) in a large animal model. METHODS Multiphasic abdominal low-kilovolt CTs (90 kV) with low (low CM, 105 mgl/kg) and normal contrast media doses (normal CM, 350 mgl/kg) were performed with 20 healthy Göttingen minipigs on 3 separate occasions for a total of 120 examinations. These included an early arterial, late arterial, portal venous, and venous contrast phase. One animal had to be excluded because of incomplete examinations. Three of the 19 animals were randomly selected and withheld for validation (18 studies). Subsequently, the GAN was trained for image-to-image conversion from low CM to normal CM (virtual CM) with the remaining 16 animals (96 examinations). For validation, region of interest measurements were performed in the abdominal aorta, inferior vena cava, portal vein, liver parenchyma, and autochthonous back muscles, and the contrast-to-noise ratio (CNR) was calculated. In addition, the normal CM and virtual CM data were presented in a visual Turing test to 3 radiology consultants. On the one hand, they had to decide which images were derived from the normal CM examination. On the other hand, they had to evaluate whether both images are pathological consistent. RESULTS Average vascular CNR (low CM 6.9 ± 7.0 vs virtual CM 28.7 ± 23.8, P < 0.0001) and parenchymal (low CM 1.5 ± 0.7 vs virtual CM 3.8 ± 2.0, P < 0.0001) CNR increased significantly by GAN-based contrast enhancement in all contrast phases and was not significantly different from normal CM examinations (vascular: virtual CM 28.7 ± 23.8 vs normal CM 34.2 ± 28.8; parenchymal: virtual CM 3.8 ± 2.0 vs normal CM 3.7 ± 2.6). During the visual Turing testing, the radiology consultants reported that images from normal CM and virtual CM were pathologically consistent in median in 96.5% of the examinations. Furthermore, it was possible for the examiners to identify the normal CM data as such in median in 91% of the cases. CONCLUSIONS In this feasibility study, it could be demonstrated in an experimental setting with healthy Göttingen minipigs that the amount of CM for abdominal CT can be reduced by approximately 70% by GAN-based contrast enhancement with satisfactory image quality.
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Affiliation(s)
- Johannes Haubold
- From the Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen
| | - Gregor Jost
- MR and CT Contrast Media Research, Bayer AG, Berlin
| | - Jens Matthias Theysohn
- From the Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen
| | - Johannes Maximilian Ludwig
- From the Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen
| | - Yan Li
- From the Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen
| | - Jens Kleesiek
- Institute of Artificial Intelligence in Medicine, University Hospital Essen, Germany
| | | | - Michael Forsting
- From the Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen
| | | | | | - René Hosch
- Institute of Artificial Intelligence in Medicine, University Hospital Essen, Germany
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Kroll L, Mathew A, Baldini G, Hosch R, Koitka S, Kleesiek J, Rischpler C, Haubold J, Fuhrer D, Nensa F, Lahner H. CT-derived body composition analysis could possibly replace DXA and BIA to monitor NET-patients. Sci Rep 2022; 12:13419. [PMID: 35927564 PMCID: PMC9352897 DOI: 10.1038/s41598-022-17611-3] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2022] [Accepted: 07/28/2022] [Indexed: 12/03/2022] Open
Abstract
Patients with neuroendocrine tumors of gastro-entero-pancreatic origin (GEP-NET) experience changes in fat and muscle composition. Dual-energy X-ray absorptiometry (DXA) and bioelectrical impedance analysis (BIA) are currently used to analyze body composition. Changes thereof could indicate cancer progression or response to treatment. This study examines the correlation between CT-based (computed tomography) body composition analysis (BCA) and DXA or BIA measurement. 74 GEP-NET-patients received whole-body [68Ga]-DOTATOC-PET/CT, BIA, and DXA-scans. BCA was performed based on the non-contrast-enhanced, 5 mm, whole-body-CT images. BCA from CT shows a strong correlation between body fat ratio with DXA (r = 0.95, ρC = 0.83) and BIA (r = 0.92, ρC = 0.76) and between skeletal muscle ratio with BIA: r = 0.81, ρC = 0.49. The deep learning-network achieves highly accurate results (mean Sørensen-Dice-score 0.93). Using BCA on routine Positron emission tomography/CT-scans to monitor patients’ body composition in the diagnostic workflow can reduce additional exams whilst substantially amplifying measurement in slower progressing cancers such as GEP-NET.
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Affiliation(s)
- Lennard Kroll
- Institute for Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany. .,Institute for Artificial Intelligence in Medicine, University Hospital Essen, Essen, Germany.
| | - Annie Mathew
- Department of Endocrinology, Diabetes and Metabolism and Division of Laboratory Research, University Hospital Essen, Essen, Germany
| | - Giulia Baldini
- Institute for Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany.,Institute for Artificial Intelligence in Medicine, University Hospital Essen, Essen, Germany
| | - René Hosch
- Institute for Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany.,Institute for Artificial Intelligence in Medicine, University Hospital Essen, Essen, Germany
| | - Sven Koitka
- Institute for Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany.,Institute for Artificial Intelligence in Medicine, University Hospital Essen, Essen, Germany
| | - Jens Kleesiek
- Institute for Artificial Intelligence in Medicine, University Hospital Essen, Essen, Germany
| | | | - Johannes Haubold
- Institute for Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany.,Institute for Artificial Intelligence in Medicine, University Hospital Essen, Essen, Germany
| | - Dagmar Fuhrer
- Department of Endocrinology, Diabetes and Metabolism and Division of Laboratory Research, University Hospital Essen, Essen, Germany
| | - Felix Nensa
- Institute for Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany.,Institute for Artificial Intelligence in Medicine, University Hospital Essen, Essen, Germany
| | - Harald Lahner
- Department of Endocrinology, Diabetes and Metabolism and Division of Laboratory Research, University Hospital Essen, Essen, Germany
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38
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Bos D, Zensen S, Opitz M, Nassenstein K, Kinner S, Schweiger B, Forsting M, Wetter A, Guberina N, Haubold J. Diagnostische Referenzwerte von Computertomographien des Thorax bei Kindern in Abhängigkeit von Patientengröße und Alter. ROFO-FORTSCHR RONTG 2022. [DOI: 10.1055/s-0042-1749891] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Affiliation(s)
- D Bos
- Universitätsklinikum Essen, Institut f. Diagn. u. Interv. Radiologie u. Neuroradiologie, Essen
| | - S Zensen
- Institut für Diagnostische und Interventionelle Radiologie und Neuroradiologie, Universitätsklinikum Essen, Essen
| | - M Opitz
- Institut für Diagnostische und Interventionelle Radiologie und Neuroradiologie, Universitätsklinikum Essen, Essen
| | - K Nassenstein
- Institut für Diagnostische und Interventionelle Radiologie und Neuroradiologie, Universitätsklinikum Essen, Essen
| | - S Kinner
- Institut für Diagnostische und Interventionelle Radiologie und Neuroradiologie, Universitätsklinikum Essen, Essen
| | - B Schweiger
- Institut für Diagnostische und Interventionelle Radiologie und Neuroradiologie, Universitätsklinikum Essen, Essen
| | - M Forsting
- Institut für Diagnostische und Interventionelle Radiologie und Neuroradiologie, Universitätsklinikum Essen, Essen
| | - A Wetter
- Klinik für Diagnostische und Interventionelle Radiologie, Neuroradiologie, Asklepios Klinikum Harburg, Hamburg
| | - N Guberina
- Klinik für Strahlentherapie, Universitätsklinikum Essen, Essen
| | - J Haubold
- Institut für Diagnostische und Interventionelle Radiologie und Neuroradiologie, Universitätsklinikum Essen, Essen
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39
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Haubold J, Nensa F, Pietsch H, Forsting M, Schaarschmidt MB, Li Y, Theysohn MJ, Ludwig MJ, Jost G, Hosch R. Kontrastmittelreduzierung in der Computertomographie mit Deep Learning unter Verwendung eines Generative Adversarial Networks in einer experimentellen Tierstudie. ROFO-FORTSCHR RONTG 2022. [DOI: 10.1055/s-0042-1749774] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Affiliation(s)
- J Haubold
- Universitätsklinikum Essen, Institut für Diagnostische und Interventionelle Radiologie u, Essen
| | - F Nensa
- Institut für künstliche Intelligenz in der Medizin, Universitätsklinikum Essen, Essen
| | - H Pietsch
- MR & CT Contrast Media Research, Bayer AG, Berlin
| | - M Forsting
- Institut für Diagnostische und Interventionelle Radiologie und Neuroradiologie, Universitätsklinikum Essen, Essen
| | - M B Schaarschmidt
- Institut für Diagnostische und Interventionelle Radiologie und Neuroradiologie, Universitätsklinikum Essen, Essen
| | - Y Li
- Institut für Diagnostische und Interventionelle Radiologie und Neuroradiologie, Universitätsklinikum Essen, Essen
| | - M J Theysohn
- Institut für Diagnostische und Interventionelle Radiologie und Neuroradiologie, Universitätsklinikum Essen, Essen
| | - M J Ludwig
- Institut für Diagnostische und Interventionelle Radiologie und Neuroradiologie, Universitätsklinikum Essen, Essen
| | - G Jost
- MR & CT Contrast Media Research, Bayer AG, Berlin
| | - R Hosch
- Institut für künstliche Intelligenz in der Medizin, Universitätsklinikum Essen, Essen
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40
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Meetschen M, Haubold J, Zeng K, Farhand S, Stalke S, Steinberg H, Bos D, Kureishi A, Zensen S, Goeser T, Maier S, Forsting M, Umutlu L, Nensa F. KI als Co-Pilot: Inhaltsbasierte Bildsuche zur Erkennung seltener Krankheiten in der Thorax-CT. ROFO-FORTSCHR RONTG 2022. [DOI: 10.1055/s-0042-1749760] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Affiliation(s)
- M Meetschen
- Uniklinik Essen, Institut für Diagnostische und Interventionelle Radiologie u, Essen
| | - J Haubold
- Institut für Diagnostische und Interventionelle Radiologie und Neuroradiologie, Universitätsklinikum Essen, Essen
| | - K Zeng
- Siemens Medical Solutions Inc., Malvern, PA
| | - S Farhand
- Siemens Medical Solutions Inc., Malvern, PA
| | - S Stalke
- Georg Thieme Verlag KG, Stuttgart
| | - H Steinberg
- Institut für Diagnostische und Interventionelle Radiologie und Neuroradiologie, Essen, Universitätsklinikum Essen, Essen
| | - D Bos
- Institut für Diagnostische und Interventionelle Radiologie und Neuroradiologie, Universitätsklinikum Essen, Essen
| | - A Kureishi
- Institut für Künstliche Intelligenz in der Medizin, Universitätsklinikum Essen, Essen
| | - S Zensen
- Institut für Diagnostische und Interventionelle Radiologie und Neuroradiologie, Universitätsklinikum Essen, Essen
| | - T Goeser
- Radiologie und Neuroradiologie, Kliniken Maria Hilf GmbH, Mönchengladbach
| | - S Maier
- Institut für Diagnostische und Interventionelle Radiologie und Neuroradiologie, Universitätsklinikum Essen, Essen
| | - M Forsting
- Institut für Diagnostische und Interventionelle Radiologie und Neuroradiologie, Universitätsklinikum Essen, Essen
| | - L Umutlu
- Institut für Diagnostische und Interventionelle Radiologie und Neuroradiologie, Universitätsklinikum Essen, Essen
| | - F Nensa
- Institut für Künstliche Intelligenz in der Medizin, Universitätsklinikum Essen, Essen
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41
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Hosch R, Weber M, Sraieb M, Flaschel N, Haubold J, Kim MS, Umutlu L, Kleesiek J, Herrmann K, Nensa F, Rischpler C, Koitka S, Seifert R, Kersting D. Artificial intelligence guided enhancement of digital PET: scans as fast as CT? Eur J Nucl Med Mol Imaging 2022; 49:4503-4515. [PMID: 35904589 PMCID: PMC9606065 DOI: 10.1007/s00259-022-05901-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Accepted: 06/30/2022] [Indexed: 12/03/2022]
Abstract
Purpose Both digital positron emission tomography (PET) detector technologies and artificial intelligence based image post-reconstruction methods allow to reduce the PET acquisition time while maintaining diagnostic quality. The aim of this study was to acquire ultra-low-count fluorodeoxyglucose (FDG) ExtremePET images on a digital PET/computed tomography (CT) scanner at an acquisition time comparable to a CT scan and to generate synthetic full-dose PET images using an artificial neural network. Methods This is a prospective, single-arm, single-center phase I/II imaging study. A total of 587 patients were included. For each patient, a standard and an ultra-low-count FDG PET/CT scan (whole-body acquisition time about 30 s) were acquired. A modified pix2pixHD deep-learning network was trained employing 387 data sets as training and 200 as test cohort. Three models (PET-only and PET/CT with or without group convolution) were compared. Detectability and quantification were evaluated. Results The PET/CT input model with group convolution performed best regarding lesion signal recovery and was selected for detailed evaluation. Synthetic PET images were of high visual image quality; mean absolute lesion SUVmax (maximum standardized uptake value) difference was 1.5. Patient-based sensitivity and specificity for lesion detection were 79% and 100%, respectively. Not-detected lesions were of lower tracer uptake and lesion volume. In a matched-pair comparison, patient-based (lesion-based) detection rate was 89% (78%) for PERCIST (PET response criteria in solid tumors)-measurable and 36% (22%) for non PERCIST-measurable lesions. Conclusion Lesion detectability and lesion quantification were promising in the context of extremely fast acquisition times. Possible application scenarios might include re-staging of late-stage cancer patients, in whom assessment of total tumor burden can be of higher relevance than detailed evaluation of small and low-uptake lesions. Supplementary Information The online version contains supplementary material available at 10.1007/s00259-022-05901-x.
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Affiliation(s)
- René Hosch
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Hufelandstraße 55, 45147, Essen, Germany. .,Institute for Artificial Intelligence in Medicine (IKIM), University Hospital Essen, Girardetstraße 2, 45131, Essen, Germany.
| | - Manuel Weber
- Department of Nuclear Medicine and German Cancer Consortium (DKTK), University Hospital Essen, University of Duisburg-Essen, Hufelandstraße 55, 45147, Essen, Germany
| | - Miriam Sraieb
- Department of Nuclear Medicine and German Cancer Consortium (DKTK), University Hospital Essen, University of Duisburg-Essen, Hufelandstraße 55, 45147, Essen, Germany
| | - Nils Flaschel
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Hufelandstraße 55, 45147, Essen, Germany.,Institute for Artificial Intelligence in Medicine (IKIM), University Hospital Essen, Girardetstraße 2, 45131, Essen, Germany
| | - Johannes Haubold
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Hufelandstraße 55, 45147, Essen, Germany
| | - Moon-Sung Kim
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Hufelandstraße 55, 45147, Essen, Germany.,Institute for Artificial Intelligence in Medicine (IKIM), University Hospital Essen, Girardetstraße 2, 45131, Essen, Germany
| | - Lale Umutlu
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Hufelandstraße 55, 45147, Essen, Germany
| | - Jens Kleesiek
- Institute for Artificial Intelligence in Medicine (IKIM), University Hospital Essen, Girardetstraße 2, 45131, Essen, Germany
| | - Ken Herrmann
- Department of Nuclear Medicine and German Cancer Consortium (DKTK), University Hospital Essen, University of Duisburg-Essen, Hufelandstraße 55, 45147, Essen, Germany
| | - Felix Nensa
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Hufelandstraße 55, 45147, Essen, Germany.,Institute for Artificial Intelligence in Medicine (IKIM), University Hospital Essen, Girardetstraße 2, 45131, Essen, Germany
| | - Christoph Rischpler
- Department of Nuclear Medicine and German Cancer Consortium (DKTK), University Hospital Essen, University of Duisburg-Essen, Hufelandstraße 55, 45147, Essen, Germany
| | - Sven Koitka
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Hufelandstraße 55, 45147, Essen, Germany.,Institute for Artificial Intelligence in Medicine (IKIM), University Hospital Essen, Girardetstraße 2, 45131, Essen, Germany
| | - Robert Seifert
- Department of Nuclear Medicine and German Cancer Consortium (DKTK), University Hospital Essen, University of Duisburg-Essen, Hufelandstraße 55, 45147, Essen, Germany.,Department of Nuclear Medicine, University Hospital Münster, University of Münster, Albert-Schweitzer-Campus 1, 48149, Münster, Germany
| | - David Kersting
- Department of Nuclear Medicine and German Cancer Consortium (DKTK), University Hospital Essen, University of Duisburg-Essen, Hufelandstraße 55, 45147, Essen, Germany
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42
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Zensen S, Opitz MK, Grueneisen JS, Li Y, Haubold J, Steinberg HL, Forsting M, Theysohn JM, Bos D, Schaarschmidt BM. Radiation exposure, organ and effective dose of CT-guided liver biopsy as a function of lesion depth and size. J Radiol Prot 2022; 42:031505. [PMID: 35790148 DOI: 10.1088/1361-6498/ac7e80] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/18/2022] [Accepted: 07/05/2022] [Indexed: 06/15/2023]
Abstract
Computed tomography (CT)-guided percutaneous biopsies play an important role in the diagnostic workup of liver lesions. Because radiation dose accumulates rapidly due to repeated image acquisition in a relatively small scan area, analysing radiation exposure is critical for improving radiation protection of CT-guided interventions. The aim of this study was to assess the radiation dose of CT-guided liver biopsies and the influence of lesion parameters, and to establish a local diagnostic reference level (DRL). In this observational retrospective cohort study, dose data of 60 CT-guided liver biopsies between September 2016 and July 2017 were analysed. Radiation exposure was reported for volume-weighted CT dose index (CTDIvol), size-specific dose estimate (SSDE), dose-length product (DLP) and effective dose (ED). Radiation dose of CT-guided liver biopsy was (median (interquartile range)): CTDIvol9.91 mGy (8.33-11.45 mGy), SSDE 10.42 mGy (9.39-11.70 mGy), DLP 542 mGy cm (410-733 mGy cm), ED 8.52 mSv (7.17-13.25 mSv). Radiation exposure was significantly higher in biopsies of deep liver lesions compared to superficial lesions (DLP 679 ± 285 mGy cm vs. 497 ± 167 mGy cm,p= 0.0046). No significant dose differences were observed for differences in lesion or needle size. With helical CT spirals additional to the biopsy-guiding axial CT scans, radiation exposure was significantly increased: 797 ± 287 mGy cm vs. 495 ± 162 mGy cm,p< 0.0001. The local DRL is CTDIvol9.91 mGy, DLP 542 mGy cm. Radiation dose is significantly increased in biopsies of deeper liver lesions compared with superficial lesions. Interventions with additional biopsy-guiding CT spirals lead to higher radiation doses. This study provides a detailed analysis of local radiation doses for CT-guided liver biopsies and provides a benchmark for optimising radiation protection in interventional radiology.
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Affiliation(s)
- Sebastian Zensen
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Hufelandstraße 55, Essen, 45147, Germany
| | - Marcel Klaus Opitz
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Hufelandstraße 55, Essen, 45147, Germany
| | - Johannes Stefan Grueneisen
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Hufelandstraße 55, Essen, 45147, Germany
| | - Yan Li
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Hufelandstraße 55, Essen, 45147, Germany
| | - Johannes Haubold
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Hufelandstraße 55, Essen, 45147, Germany
| | - Hannah Louisa Steinberg
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Hufelandstraße 55, Essen, 45147, Germany
| | - Michael Forsting
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Hufelandstraße 55, Essen, 45147, Germany
| | - Jens Matthias Theysohn
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Hufelandstraße 55, Essen, 45147, Germany
| | - Denise Bos
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Hufelandstraße 55, Essen, 45147, Germany
| | - Benedikt Michael Schaarschmidt
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Hufelandstraße 55, Essen, 45147, Germany
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43
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Schaarschmidt BM, Fistera D, Li Y, Konik M, Haubold J, Grueneisen J, Witzke O, Forsting M, Holzner C, Umutlu L. Streamlining Patient Management of Suspected COVID-19 Patients in the Emergency Department: Incorporation of Pulmonary CT Angiography into the Triaging Algorithm. Diagnostics (Basel) 2022; 12:diagnostics12051183. [PMID: 35626338 PMCID: PMC9140044 DOI: 10.3390/diagnostics12051183] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Revised: 04/26/2022] [Accepted: 05/06/2022] [Indexed: 11/23/2022] Open
Abstract
Purpose: To evaluate the use of pulmonary computed tomography (CT) angiography during initial admission at an emergency department (ED), to identify COVID-19 patients with accompanying pulmonary embolism (PE) and its impact on clinical management. Methods: We performed a retrospective analysis of COVID-19 patients that underwent pulmonary CT angiography at the ED. CT scans were evaluated for the presence and extent of PE and for imaging changes suspicious of COVID-19. Patients were subdivided into two groups: (1) Group A consisted of patients with proven COVID-19 based on real-time polymerase chain reaction (RT-PCR), and (2) Group B of patients suspected for COVID-19, comprising patients positive on RT-PCR and/or COVID-19-suspicious CT findings. To assess the differences between patients with and without pulmonary embolism, Fisher’s exact test was used. Results: A total of 308 patients were admitted to the ED for diagnostic work-up of dyspnea and suspected COVID-19, and 95 patients underwent pulmonary CT angiography. PE was detected in 13.6% (3/22) of patients in Group A and 20.7% (6/29) in Group B. No significant differences were observed between patients with and without PE concerning hospitalization (Group B: 100% (6/6) vs. 91.3% (21/23)), the necessity of oxygen therapy (Group B: 66% (4/6) vs. 43.5% (10/23)), and death (Group B: 33% (2/6) vs. 4.3% (1/23) p > 0.05, respectively). Conclusions: In 20.7% of COVID-19 patients, PE was detected upon admission to the ED. Although the incorporation of early pulmonary CT angiography in patients suspicious of COVID-19 may be beneficial to identify concomitant PE, further studies are necessary to corroborate these findings.
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Affiliation(s)
- Benedikt M. Schaarschmidt
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, 45147 Essen, Germany; (Y.L.); (J.H.); (J.G.); (M.F.); (L.U.)
- Correspondence: ; Tel.: +49-201-723-84168
| | - David Fistera
- Center for Emergency Medicine, Universitätsmedizin Essen, 45147 Essen, Germany; (D.F.); (C.H.)
| | - Yan Li
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, 45147 Essen, Germany; (Y.L.); (J.H.); (J.G.); (M.F.); (L.U.)
| | - Margarete Konik
- Department of Infectious Diseases, West German Centre of Infectious Diseases, Universitätsmedizin Essen, 45147 Essen, Germany; (M.K.); (O.W.)
| | - Johannes Haubold
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, 45147 Essen, Germany; (Y.L.); (J.H.); (J.G.); (M.F.); (L.U.)
| | - Johannes Grueneisen
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, 45147 Essen, Germany; (Y.L.); (J.H.); (J.G.); (M.F.); (L.U.)
| | - Oliver Witzke
- Department of Infectious Diseases, West German Centre of Infectious Diseases, Universitätsmedizin Essen, 45147 Essen, Germany; (M.K.); (O.W.)
| | - Michael Forsting
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, 45147 Essen, Germany; (Y.L.); (J.H.); (J.G.); (M.F.); (L.U.)
| | - Carola Holzner
- Center for Emergency Medicine, Universitätsmedizin Essen, 45147 Essen, Germany; (D.F.); (C.H.)
| | - Lale Umutlu
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, 45147 Essen, Germany; (Y.L.); (J.H.); (J.G.); (M.F.); (L.U.)
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44
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Zensen S, Opitz MK, Ludwig JM, Haubold J, Richly H, Siveke JT, Theysohn JM, Forsting M, Bos D, Schaarschmidt BM. Radiation Dose Aspects of Hepatic Artery Infusion Chemotherapy in Uveal Melanoma Patients with Liver Metastases. Cardiovasc Intervent Radiol 2022; 45:841-845. [PMID: 35437708 PMCID: PMC9117360 DOI: 10.1007/s00270-022-03130-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/09/2022] [Accepted: 03/19/2022] [Indexed: 12/03/2022]
Abstract
Purpose In uveal melanoma patients, liver metastases can be treated by hepatic artery infusion chemotherapy (HAIC). During this procedure, melphalan or, less frequently, fotemustine is infused into the hepatic artery or the hepatic lobe arteries in regularly repeated interventions to achieve local tumor control. The aim of this study was to investigate the radiation exposure of HAIC. Material and methods In this retrospective study, dose data from 841 procedures in 140 patients (mean age 65.3 ± 9.9 years, 74 female) who underwent HAIC between 06/2017 and 10/2021 at one of three different angiography systems were analyzed. Results In the overall population, dose area product (DAP) (median (IQR)) was 1773 cGy·cm2 (884–3688). DAP was significantly higher in the first intervention, where a complete diagnostic workup of the vasculature was performed, than in follow-up interventions: 5765 cGy·cm2 (3160–8804) versus 1502 cGy·cm2 (807–2712) (p < 0.0001). DAP also increased significantly with the number of infusion positions (median, (IQR)): one position 1301 cGy·cm2 (633–2717), two positions 1985 cGy·cm2 (1118–4074), three positions 6407 cGy·cm2 (2616–11590) (p < 0.0001). Conclusion In uveal melanoma patients with liver metastases undergoing HAIC, radiation exposure is significantly higher both at the first intervention compared to follow-up interventions, but also with increasing number of infusion positions. Level of evidence: 3
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Affiliation(s)
- Sebastian Zensen
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Hufelandstraße 55, 45147, Essen, Germany.
| | - Marcel K Opitz
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Hufelandstraße 55, 45147, Essen, Germany
| | - Johannes M Ludwig
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Hufelandstraße 55, 45147, Essen, Germany
| | - Johannes Haubold
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Hufelandstraße 55, 45147, Essen, Germany
| | - Heike Richly
- Department of Medical Oncology, West German Cancer Center, University of Duisburg-Essen, Essen, Germany
| | - Jens T Siveke
- Bridge Institute of Experimental Tumor Therapy, West German Cancer Center, University Medicine Essen, Essen, Germany.,Division of Solid Tumor Translational Oncology, German Cancer Research Center (DKFZ) and German Cancer Consortium (DKTK), Partner Site Essen, Heidelberg, Germany
| | - Jens M Theysohn
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Hufelandstraße 55, 45147, Essen, Germany
| | - Michael Forsting
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Hufelandstraße 55, 45147, Essen, Germany
| | - Denise Bos
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Hufelandstraße 55, 45147, Essen, Germany
| | - Benedikt M Schaarschmidt
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Hufelandstraße 55, 45147, Essen, Germany
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45
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Zensen S, Bos D, Opitz M, Haubold J, Forsting M, Guberina N, Wetter A. Radiation exposure and establishment of diagnostic reference levels of whole-body low-dose CT for the assessment of multiple myeloma with second- and third-generation dual-source CT. Acta Radiol 2022; 63:527-535. [PMID: 33745295 DOI: 10.1177/02841851211003287] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND In the assessment of diseases causing skeletal lesions such as multiple myeloma (MM), whole-body low-dose computed tomography (WBLDCT) is a sensitive diagnostic imaging modality, which has the potential to replace the conventional radiographic survey. PURPOSE To optimize radiation protection and examine radiation exposure, and effective and organ doses of WBLDCT using different modern dual-source CT (DSCT) devices, and to establish local diagnostic reference levels (DRL). MATERIAL AND METHODS In this retrospective study, 281 WBLDCT scans of 232 patients performed between January 2017 and April 2020 either on a second- (A) or third-generation (B) DSCT device could be included. Radiation exposure indices and organ and effective doses were calculated using a commercially available automated dose-tracking software based on Monte-Carlo simulation techniques. RESULTS The radiation exposure indices and effective doses were distributed as follows (median, interquartile range): (A) second-generation DSCT: volume-weighted CT dose index (CTDIvol) 1.78 mGy (1.47-2.17 mGy); dose length product (DLP) 282.8 mGy·cm (224.6-319.4 mGy·cm), effective dose (ED) 1.87 mSv (1.61-2.17 mSv) and (B) third-generation DSCT: CTDIvol 0.56 mGy (0.47-0.67 mGy), DLP 92.0 mGy·cm (73.7-107.6 mGy·cm), ED 0.61 mSv (0.52-0.69 mSv). Radiation exposure indices and effective and organ doses were significantly lower with third-generation DSCT (P < 0.001). Local DRLs could be set for CTDIvol at 0.75 mGy and DLP at 120 mGy·cm. CONCLUSION Third-generation DSCT requires significantly lower radiation dose for WBLDCT than second-generation DSCT and has an effective dose below reported doses for radiographic skeletal surveys. To ensure radiation protection, DRLs regarding WBLDCT are required, where our locally determined values may help as benchmarks.
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Affiliation(s)
- Sebastian Zensen
- Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany
| | - Denise Bos
- Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany
| | - Marcel Opitz
- Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany
| | - Johannes Haubold
- Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany
| | - Michael Forsting
- Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany
| | - Nika Guberina
- Department of Radiotherapy, University Hospital Essen, Essen, Germany
| | - Axel Wetter
- Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany
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46
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Müller L, Hahn F, Auer TA, Fehrenbach U, Gebauer B, Haubold J, Zensen S, Kim MS, Eisenblätter M, Diallo TD, Bettinger D, Steinle V, Chang DH, Zopfs D, Pinto Dos Santos D, Kloeckner R. Tumor Burden in Patients With Hepatocellular Carcinoma Undergoing Transarterial Chemoembolization: Head-to-Head Comparison of Current Scoring Systems. Front Oncol 2022; 12:850454. [PMID: 35280804 PMCID: PMC8904349 DOI: 10.3389/fonc.2022.850454] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2022] [Accepted: 01/28/2022] [Indexed: 12/13/2022] Open
Abstract
Objectives Recently, several scoring systems for prognosis prediction based on tumor burden have been promoted for patients with hepatocellular carcinoma (HCC) undergoing transarterial chemoembolization (TACE). This multicenter study aimed to perform the first head-to-head comparison of three scoring systems. Methods We retrospectively enrolled 849 treatment-naïve patients with HCC undergoing TACE at six tertiary care centers between 2010 and 2020. The tumor burden score (TBS), the Six-and-Twelve score (SAT), and the Seven-Eleven criteria (SEC) were calculated based on the maximum lesion size and the number of tumor nodes. All scores were compared in univariate and multivariate regression analyses, adjusted for established risk factors. Results The median overall survival (OS) times were 33.0, 18.3, and 12.8 months for patients with low, medium, and high TBS, respectively (p<0.001). The median OS times were 30.0, 16.9, and 10.2 months for patients with low, medium, and high SAT, respectively (p<0.001). The median OS times were 27.0, 16.7, and 10.5 for patients with low, medium, and high SEC, respectively (p<0.001). In a multivariate analysis, only the SAT remained an independent prognostic factor. The C-Indexes were 0.54 for the TBS, 0.59 for the SAT, and 0.58 for the SEC. Conclusion In a direct head-to-head comparison, the SAT was superior to the TBS and SEC in survival stratification and predictive ability. Therefore, the SAT can be considered when estimating the tumor burden. However, all three scores showed only moderate predictive power. Therefore, tumor burden should only be one component among many in treatment decision making.
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Affiliation(s)
- Lukas Müller
- Department of Diagnostic and Interventional Radiology, University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany
| | - Felix Hahn
- Department of Diagnostic and Interventional Radiology, University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany
| | - Timo Alexander Auer
- Department of Radiology, Charité - University Medicine Berlin, Berlin, Germany
| | - Uli Fehrenbach
- Department of Radiology, Charité - University Medicine Berlin, Berlin, Germany
| | - Bernhard Gebauer
- Department of Radiology, Charité - University Medicine Berlin, Berlin, Germany
| | - Johannes Haubold
- Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany
| | - Sebastian Zensen
- Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany
| | - Moon-Sung Kim
- Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany
| | - Michel Eisenblätter
- Department of Diagnostic and Interventional Radiology, Freiburg University Hospital, Freiburg, Germany
| | - Thierno D Diallo
- Department of Diagnostic and Interventional Radiology, Freiburg University Hospital, Freiburg, Germany
| | - Dominik Bettinger
- Department of Medicine II, Medical Center University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Verena Steinle
- Department of Diagnostic and Interventional Radiology, University Medical Center Heidelberg, Heidelberg, Germany
| | - De-Hua Chang
- Department of Diagnostic and Interventional Radiology, University Medical Center Heidelberg, Heidelberg, Germany
| | - David Zopfs
- Department of Radiology, University Hospital Cologne, Cologne, Germany
| | | | - Roman Kloeckner
- Department of Diagnostic and Interventional Radiology, University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany
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47
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Bos D, Zensen S, Opitz MK, Haubold J, Nassenstein K, Kinner S, Schweiger B, Forsting M, Wetter A, Guberina N. Diagnostic reference levels for chest computed tomography in children as a function of patient size. Pediatr Radiol 2022; 52:1446-1455. [PMID: 35378606 PMCID: PMC9271112 DOI: 10.1007/s00247-022-05340-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/21/2021] [Revised: 02/11/2022] [Accepted: 02/25/2022] [Indexed: 11/10/2022]
Abstract
BACKGROUND Radiation exposures from computed tomography (CT) in children are inadequately studied. Diagnostic reference levels (DRLs) can help optimise radiation doses. OBJECTIVE To determine local DRLs for paediatric chest CT performed mainly on modern dual-source, multi-slice CT scanners as a function of patient size. MATERIALS AND METHODS Five hundred thirty-eight chest CT scans in 345 children under 15 years (y) of age (median age: 8 y, interquartile range [IQR]: 4-13 y) performed on four different CT scanners (38% on third-generation and 43% on second-generation dual-source CT) between November 2013 and December 2020 were retrospectively analysed. Examinations were grouped by water-equivalent diameter as a measure of patient size. DRLs for volume CT dose index (CTDIvol) and dose-length product (DLP) were determined for six different patient sizes and compared to national and European DRLs. RESULTS The DRLs for CTDIvol and DLP are determined for each patient size group as a function of water-equivalent diameter as follows: (I) < 13 cm (n = 22; median: age 7 months): 0.4 mGy, 7 mGy·cm; (II) 13 cm to less than 17 cm (n = 151; median: age 3 y): 1.2 mGy, 25 mGy·cm; (III) 17 cm to less than 21 cm (n = 211; median: age 8 y): 1.7 mGy, 44 mGy·cm; (IV) 21 cm to less than 25 cm (n = 97; median: age 14 y): 3.0 mGy, 88 mGy·cm; (V) 25 cm to less than 29 cm (n = 42; median: age 14 y): 4.5 mGy, 135 mGy·cm; (VI) ≥ 29 cm (n = 15; median: age 14 y): 8.0 mGy, 241 mGy·cm. Compared with corresponding age and weight groups, our size-based DRLs for DLP are 54% to 71% lower than national and 23% to 85% lower than European DRLs. CONCLUSION We developed DRLs for paediatric chest CT as a function of patient size with substantially lower values than national and European DRLs. Precise knowledge of size-based DRLs may assist other institutions in further dose optimisation in children.
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Affiliation(s)
- Denise Bos
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, University of Duisburg-Essen, Hufelandstraße 55, 45147, Essen, Germany.
| | - Sebastian Zensen
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, University of Duisburg-Essen, Hufelandstraße 55, 45147, Essen, Germany
| | - Marcel K Opitz
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, University of Duisburg-Essen, Hufelandstraße 55, 45147, Essen, Germany
| | - Johannes Haubold
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, University of Duisburg-Essen, Hufelandstraße 55, 45147, Essen, Germany
| | - Kai Nassenstein
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, University of Duisburg-Essen, Hufelandstraße 55, 45147, Essen, Germany
| | - Sonja Kinner
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, University of Duisburg-Essen, Hufelandstraße 55, 45147, Essen, Germany
| | - Bernd Schweiger
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, University of Duisburg-Essen, Hufelandstraße 55, 45147, Essen, Germany
| | - Michael Forsting
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, University of Duisburg-Essen, Hufelandstraße 55, 45147, Essen, Germany
| | - Axel Wetter
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, University of Duisburg-Essen, Hufelandstraße 55, 45147, Essen, Germany
- Department of Diagnostic and Interventional Radiology, Neuroradiology, Asklepios Klinikum Harburg, Eißendorfer Pferdeweg 52, 21075, Hamburg, Germany
| | - Nika Guberina
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, University of Duisburg-Essen, Hufelandstraße 55, 45147, Essen, Germany
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48
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Haubold J, Zensen S, Erfanian Y, Guberina N, Opitz M, Sawicki LM, Forsting M, Umutlu L, Theysohn JM. ULTRA-LOW-DOSE COMPUTED TOMOGRAPHY IN UROLITHIASIS-EFFECT OF AN ADDITIONAL TIN FILTER ON IMAGE QUALITY AND RADIATION DOSE. Radiat Prot Dosimetry 2021; 197:146-153. [PMID: 34952539 DOI: 10.1093/rpd/ncab180] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/23/2021] [Revised: 11/12/2021] [Accepted: 12/01/2021] [Indexed: 06/14/2023]
Abstract
To compare radiation dose and image quality of three CT-scanners using optimal dose protocols in patients with suspected urolithiasis regarding additional hardware (tin filter) and software (iterative reconstruction). Examinations from a single-source CT-scanner (A2) and a dual-source CT-scanner (DSCT) (A1) were compared to a tin filter DSCT (B) regarding dose-length product (DLP) and volume-weighted CT dose-index (CTDIvol). DLP of B was 51 and 53% lower in comparison to A1 and A2 (78.62, 159.20 and 165.80 mGy·cm, respectively; P < 0.0001). CTDIvol of B was 53% and 56% significantly lower compared to A1 and A2, respectively (1.52 vs. 3.22 vs. 3.46 mGy; P < 0.0001). Image quality in B proved to be similar to A1 and A2 (3.57, 3.51 and 3.60, respectively; P > 0.05). Inter-rater agreement regarding image quality was good for all CT-scanners (κ = 0.62). Modern CTs with a built-in tin filter allow a significant reduction of radiation exposure in patients with suspected urolithiasis by optimizing the X-ray spectrum.
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Haubold J, Hosch R, Parmar V, Glas M, Guberina N, Catalano OA, Pierscianek D, Wrede K, Deuschl C, Forsting M, Nensa F, Flaschel N, Umutlu L. Fully Automated MR Based Virtual Biopsy of Cerebral Gliomas. Cancers (Basel) 2021; 13:cancers13246186. [PMID: 34944806 PMCID: PMC8699054 DOI: 10.3390/cancers13246186] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2021] [Revised: 11/23/2021] [Accepted: 11/28/2021] [Indexed: 11/16/2022] Open
Abstract
OBJECTIVE The aim of this study was to investigate the diagnostic accuracy of a radiomics analysis based on a fully automated segmentation and a simplified and robust MR imaging protocol to provide a comprehensive analysis of the genetic profile and grading of cerebral gliomas for everyday clinical use. METHODS MRI examinations of 217 therapy-naïve patients with cerebral gliomas, each comprising a non-contrast T1-weighted, FLAIR and contrast-enhanced T1-weighted sequence, were included in the study. In addition, clinical and laboratory parameters were incorporated into the analysis. The BraTS 2019 pretrained DeepMedic network was used for automated segmentation. The segmentations generated by DeepMedic were evaluated with 200 manual segmentations with a DICE score of 0.8082 ± 0.1321. Subsequently, the radiomics signatures were utilized to predict the genetic profile of ATRX, IDH1/2, MGMT and 1p19q co-deletion, as well as differentiating low-grade glioma from high-grade glioma. RESULTS The network provided an AUC (validation/test) for the differentiation between low-grade gliomas vs. high-grade gliomas of 0.981 ± 0.015/0.885 ± 0.02. The best results were achieved for the prediction of the ATRX expression loss with AUCs of 0.979 ± 0.028/0.923 ± 0.045, followed by 0.929 ± 0.042/0.861 ± 0.023 for the prediction of IDH1/2. The prediction of 1p19q and MGMT achieved moderate results, with AUCs of 0.999 ± 0.005/0.711 ± 0.128 for 1p19q and 0.854 ± 0.046/0.742 ± 0.050 for MGMT. CONCLUSION This fully automated approach utilizing simplified MR protocols to predict the genetic profile and grading of cerebral gliomas provides an easy and efficient method for non-invasive tumor decoding.
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Affiliation(s)
- Johannes Haubold
- Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Hufelandstr. 55, D-45147 Essen, Germany; (R.H.); (V.P.); (C.D.); (M.F.); (F.N.); (N.F.); (L.U.)
- Correspondence: ; Tel.: +49-201-723-84528; Fax: +49-201-723-1548
| | - René Hosch
- Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Hufelandstr. 55, D-45147 Essen, Germany; (R.H.); (V.P.); (C.D.); (M.F.); (F.N.); (N.F.); (L.U.)
| | - Vicky Parmar
- Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Hufelandstr. 55, D-45147 Essen, Germany; (R.H.); (V.P.); (C.D.); (M.F.); (F.N.); (N.F.); (L.U.)
| | - Martin Glas
- Department of Neurology, Division of Clinical Neurooncology, University Hospital Essen, D-45147 Essen, Germany;
| | - Nika Guberina
- Department of Radiotherapy, University Hospital Essen, D-45147 Essen, Germany;
| | - Onofrio Antonio Catalano
- Department of Radiology, Division of Abdominal Imaging, A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard University Medical School, Boston 02114, MA, USA;
| | - Daniela Pierscianek
- Department of Neurosurgery, University Hospital Essen, D-45147 Essen, Germany; (D.P.); (K.W.)
| | - Karsten Wrede
- Department of Neurosurgery, University Hospital Essen, D-45147 Essen, Germany; (D.P.); (K.W.)
| | - Cornelius Deuschl
- Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Hufelandstr. 55, D-45147 Essen, Germany; (R.H.); (V.P.); (C.D.); (M.F.); (F.N.); (N.F.); (L.U.)
| | - Michael Forsting
- Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Hufelandstr. 55, D-45147 Essen, Germany; (R.H.); (V.P.); (C.D.); (M.F.); (F.N.); (N.F.); (L.U.)
| | - Felix Nensa
- Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Hufelandstr. 55, D-45147 Essen, Germany; (R.H.); (V.P.); (C.D.); (M.F.); (F.N.); (N.F.); (L.U.)
| | - Nils Flaschel
- Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Hufelandstr. 55, D-45147 Essen, Germany; (R.H.); (V.P.); (C.D.); (M.F.); (F.N.); (N.F.); (L.U.)
| | - Lale Umutlu
- Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Hufelandstr. 55, D-45147 Essen, Germany; (R.H.); (V.P.); (C.D.); (M.F.); (F.N.); (N.F.); (L.U.)
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50
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Darr C, Fragoso Costa P, Kesch C, Krafft U, Püllen L, Harke NN, Hess J, Szarvas T, Haubold J, Reis H, Fendler WP, Herrmann K, Radtke JP, Hadaschik BA, Tschirdewahn S. Prostate specific membrane antigen-radio guided surgery using Cerenkov luminescence imaging-utilization of a short-pass filter to reduce technical pitfalls. Transl Androl Urol 2021; 10:3972-3985. [PMID: 34804840 PMCID: PMC8575587 DOI: 10.21037/tau-20-1141] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2020] [Accepted: 12/07/2020] [Indexed: 12/27/2022] Open
Abstract
Background Intraoperative Cerenkov luminescence imaging (CLI) is a novel technique to assess surgical margins in patients undergoing nerve sparing radical prostatectomy (RP). Here, we analyze the efficacy of a 550-nm optical short-pass filter (OF) to improve its performance. Methods In this prospective single-center feasibility study ten patients with prostate cancer (PC) were included between December 2019 and April 2020, including three patients without tracer injection as a control group. After preoperative injection of 68-Ga-prostate-specific membrane antigen (PSMA)-11 followed by RP, CLI of the excised prostate and the incised index lesion was performed to visualize the primary tumor lesion. We compared the findings on intraoperative CLI to postoperative histopathology. Furthermore, CLI-intensities determined as tumor to background ratio (TBR) and contrast to noise ratio (CNR) were measured. Results Histopathology proved positive surgical margins (PSM) in 3 patients with corresponding findings in CLI. After magnetic resonance imaging (MRI)-informed incision above the index lesion 2 out of 3 prostates demonstrated elevated CLI signals with histopathological confirmation of PC cells. The use of the OF enabled a significant reduction of the area of the regions of interest from a median of 1.80 to 0.15 cm2 (reduction by 85%, P=0.005) leading to increased specificity. Signals due to PSMs were not suppressed by the 550-nm OF. The median TBR was reduced from 3.33 to 2.10. In all three patients of the control group elevated CLI intensities were detected at locations with diathermal energy deposition during surgery. After application of the 550-nm OF these were almost totally suppressed with a TBR of 1.10. Measurements of Cerenkov luminescence intensity with the 550-nm OF showed a significant Pearson's correlation of 0.82 between PSM and the elevated TBR (P=0.003) and a significant Pearson's correlation of 0.66 between PSM and elevated CNR (P=0.04). Measurements without the OF did not correlate significantly. Conclusions Intraoperative 68-Ga-PSMA CLI in PC is a tool that warrants further investigation to visualize PSM especially in intermediate and high-risk PC. Intraoperative CLI benefits from usage of a 550-nm OF to reduce false-positive signals.
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Affiliation(s)
- Christopher Darr
- Department of Urology, University Hospital Essen, Essen, Germany.,German Cancer Consortium (DKTK)-University Hospital Essen, Essen, Germany
| | - Pedro Fragoso Costa
- German Cancer Consortium (DKTK)-University Hospital Essen, Essen, Germany.,Department of Nuclear Medicine, University Hospital Essen, Essen, Germany
| | - Claudia Kesch
- Department of Urology, University Hospital Essen, Essen, Germany.,German Cancer Consortium (DKTK)-University Hospital Essen, Essen, Germany
| | - Ulrich Krafft
- Department of Urology, University Hospital Essen, Essen, Germany.,German Cancer Consortium (DKTK)-University Hospital Essen, Essen, Germany
| | - Lukas Püllen
- Department of Urology, University Hospital Essen, Essen, Germany.,German Cancer Consortium (DKTK)-University Hospital Essen, Essen, Germany
| | - Nina Natascha Harke
- Department of Urology, University Hospital Essen, Essen, Germany.,German Cancer Consortium (DKTK)-University Hospital Essen, Essen, Germany
| | - Jochen Hess
- Department of Urology, University Hospital Essen, Essen, Germany.,German Cancer Consortium (DKTK)-University Hospital Essen, Essen, Germany
| | - Tibor Szarvas
- Department of Urology, University Hospital Essen, Essen, Germany.,German Cancer Consortium (DKTK)-University Hospital Essen, Essen, Germany
| | - Johannes Haubold
- German Cancer Consortium (DKTK)-University Hospital Essen, Essen, Germany.,Institute of Diagnostics and Radiology, University Hospital Essen, Essen, Germany
| | - Henning Reis
- German Cancer Consortium (DKTK)-University Hospital Essen, Essen, Germany.,Institute of Pathology, University of Duisburg-Essen, Essen, Germany
| | - Wolfgang Peter Fendler
- German Cancer Consortium (DKTK)-University Hospital Essen, Essen, Germany.,Department of Nuclear Medicine, University Hospital Essen, Essen, Germany
| | - Ken Herrmann
- German Cancer Consortium (DKTK)-University Hospital Essen, Essen, Germany.,Department of Nuclear Medicine, University Hospital Essen, Essen, Germany
| | - Jan Philipp Radtke
- Department of Urology, University Hospital Essen, Essen, Germany.,German Cancer Consortium (DKTK)-University Hospital Essen, Essen, Germany
| | - Boris Alexander Hadaschik
- Department of Urology, University Hospital Essen, Essen, Germany.,German Cancer Consortium (DKTK)-University Hospital Essen, Essen, Germany
| | - Stephan Tschirdewahn
- Department of Urology, University Hospital Essen, Essen, Germany.,German Cancer Consortium (DKTK)-University Hospital Essen, Essen, Germany
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