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Salhöfer L, Bonella F, Meetschen M, Umutlu L, Forsting M, Schaarschmidt BM, Opitz MK, Kleesiek J, Hosch R, Koitka S, Parmar V, Nensa F, Haubold J. Automated 3D-Body Composition Analysis as a Predictor of Survival in Patients With Idiopathic Pulmonary Fibrosis. J Thorac Imaging 2024:00005382-990000000-00148. [PMID: 39183570 DOI: 10.1097/rti.0000000000000803] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/27/2024]
Abstract
PURPOSE Idiopathic pulmonary fibrosis (IPF) is the most common interstitial lung disease, with a median survival time of 2 to 5 years. The focus of this study is to establish a novel imaging biomarker. MATERIALS AND METHODS In this study, 79 patients (19% female) with a median age of 70 years were studied retrospectively. Fully automated body composition analysis (BCA) features (bone, muscle, total adipose tissue, intermuscular, and intramuscular adipose tissue) were combined into Sarcopenia, Fat, and Myosteatosis indices and compared between patients with a survival of more or less than 2 years. In addition, we divided the cohort at the median (high=≥ median, low= RESULTS A high Sarcopenia and Fat index and low Myosteatosis index were associated with longer median survival (35 vs. 16 mo for high vs. low Sarcopenia index, P=0.066; 44 vs. 14 mo for high vs. low Fat index, P<0.001; and 33 vs. 14 mo for low vs. high Myosteatosis index, P=0.0056) and better 5-year survival rates (34.0% vs. 23.6% for high vs. low Sarcopenia index; 47.3% vs. 9.2% for high vs. low Fat index; and 11.2% vs. 42.7% for high vs. low Myosteatosis index). Adjusted multivariate Cox regression showed a significant impact of the Fat (HR=0.71, P=0.01) and Myosteatosis (HR=1.12, P=0.005) on overall survival. CONCLUSION The fully automated BCA provides biomarkers with a predictive value for the overall survival in patients with IPF.
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Affiliation(s)
- Luca Salhöfer
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany
- Institute for Artificial Intelligence in Medicine, University Hospital Essen, Essen, Germany
| | - Francesco Bonella
- Department of Pneumology, Center for Interstitial and Rare Lung Diseases, University Hospital Essen, Essen, Germany
| | - Mathias Meetschen
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany
- Institute for Artificial Intelligence in Medicine, University Hospital Essen, Essen, Germany
| | - Lale Umutlu
- 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
| | | | - Marcel Klaus Opitz
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany
| | - Jens Kleesiek
- Institute for Artificial Intelligence in Medicine, University Hospital Essen, Essen, Germany
| | - Rene Hosch
- Institute of 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 of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany
- Institute for Artificial Intelligence in Medicine, University Hospital Essen, Essen, Germany
| | - Vicky Parmar
- Institute for 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 for Artificial Intelligence in Medicine, University Hospital Essen, Essen, Germany
| | - Johannes Haubold
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany
- Institute for Artificial Intelligence in Medicine, University Hospital Essen, Essen, Germany
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Koitka S, Baldini G, Kroll L, van Landeghem N, Pollok OB, Haubold J, Pelka O, Kim M, Kleesiek J, Nensa F, Hosch R. SAROS: A dataset for whole-body region and organ segmentation in CT imaging. Sci Data 2024; 11:483. [PMID: 38729970 PMCID: PMC11087485 DOI: 10.1038/s41597-024-03337-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2023] [Accepted: 05/01/2024] [Indexed: 05/12/2024] Open
Abstract
The Sparsely Annotated Region and Organ Segmentation (SAROS) dataset was created using data from The Cancer Imaging Archive (TCIA) to provide a large open-access CT dataset with high-quality annotations of body landmarks. In-house segmentation models were employed to generate annotation proposals on randomly selected cases from TCIA. The dataset includes 13 semantic body region labels (abdominal/thoracic cavity, bones, brain, breast implant, mediastinum, muscle, parotid/submandibular/thyroid glands, pericardium, spinal cord, subcutaneous tissue) and six body part labels (left/right arm/leg, head, torso). Case selection was based on the DICOM series description, gender, and imaging protocol, resulting in 882 patients (438 female) for a total of 900 CTs. Manual review and correction of proposals were conducted in a continuous quality control cycle. Only every fifth axial slice was annotated, yielding 20150 annotated slices from 28 data collections. For the reproducibility on downstream tasks, five cross-validation folds and a test set were pre-defined. The SAROS dataset serves as an open-access resource for training and evaluating novel segmentation models, covering various scanner vendors and diseases.
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Affiliation(s)
- Sven Koitka
- Institute of Interventional and Diagnostic Radiology and Neuroradiology, University Hospital Essen, Essen, Germany
- Institute for Artificial Intelligence in Medicine, University Hospital Essen, Essen, Germany
| | - Giulia Baldini
- Institute of Interventional and Diagnostic Radiology and Neuroradiology, University Hospital Essen, Essen, Germany
- Institute for Artificial Intelligence in Medicine, University Hospital Essen, Essen, Germany
| | - Lennard Kroll
- Institute of Interventional and Diagnostic Radiology and Neuroradiology, University Hospital Essen, Essen, Germany
| | - Natalie van Landeghem
- Institute of Interventional and Diagnostic Radiology and Neuroradiology, University Hospital Essen, Essen, Germany
| | - Olivia B Pollok
- Institute for Artificial Intelligence in Medicine, University Hospital Essen, Essen, Germany
| | - Johannes Haubold
- Institute of Interventional and Diagnostic Radiology and Neuroradiology, University Hospital Essen, Essen, Germany
| | - Obioma Pelka
- Institute for Artificial Intelligence in Medicine, University Hospital Essen, Essen, Germany
- Data Integration Center, Central IT Department, University Hospital Essen, Essen, Germany
| | - Moon Kim
- Institute for Artificial Intelligence in Medicine, University Hospital Essen, Essen, Germany
| | - Jens Kleesiek
- Institute for Artificial Intelligence in Medicine, University Hospital Essen, Essen, Germany
| | - Felix Nensa
- Institute of Interventional and Diagnostic Radiology and Neuroradiology, University Hospital Essen, Essen, Germany
- Institute for Artificial Intelligence in Medicine, University Hospital Essen, Essen, Germany
| | - René Hosch
- Institute of Interventional and Diagnostic Radiology and Neuroradiology, University Hospital Essen, Essen, Germany.
- Institute for Artificial Intelligence in Medicine, University Hospital Essen, Essen, Germany.
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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] [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] [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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Machado MAD, Moraes TF, Anjos BHL, Alencar NRG, Chang TMC, Santana BCRF, Menezes VO, Vieira LO, Brandão SCS, Salvino MA, Netto EM. Association between increased Subcutaneous Adipose Tissue Radiodensity and cancer mortality: Automated computation, comparison of cancer types, gender, and scanner bias. Appl Radiat Isot 2024; 205:111181. [PMID: 38244325 DOI: 10.1016/j.apradiso.2024.111181] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2023] [Revised: 12/17/2023] [Accepted: 01/04/2024] [Indexed: 01/22/2024]
Abstract
PURPOSE Body composition analysis using computed tomography (CT) is proposed as a predictor of cancer mortality. An association between subcutaneous adipose tissue radiodensity (SATr) and cancer-specific mortality was established, while gender effects and equipment bias were estimated. METHODS 7,475 CT studies were selected from 17 cohorts containing CT images of untreated cancer patients who underwent follow-up for a period of 2.1-118.8 months. SATr measures were collected from published data (n = 6,718) or calculated according to CT images using a deep-learning network (n = 757). The association between SATr and mortality was ascertained for each cohort and gender using the p-value from either logistic regression or ROC analysis. The Kruskal-Wallis test was used to analyze differences between gender distributions, and automatic segmentation was evaluated using the Dice score and five-point Likert quality scale. Gender effect, scanner bias and changes in the Hounsfield unit (HU) to detect hazards were also estimated. RESULTS Higher SATr was associated with mortality in eight cancer types (p < 0.05). Automatic segmentation produced a score of 0.949 while the quality scale measurement was good to excellent. The extent of gender effect was 5.2 HU while the scanner bias was 10.3 HU. The minimum proposed HU change to detect a patient at risk of death was between 5.6 and 8.3 HU. CONCLUSIONS CT imaging provides valuable assessments of body composition as part of the staging process for several cancer types, saving both time and cost. Gender specific scales and scanner bias adjustments should be carried out to successfully implement SATr measures in clinical practice.
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Affiliation(s)
- Marcos A D Machado
- Department of Radiology, Complexo Hospitalar Universitário Prof. Edgard Santos/ Ebserh, Universidade Federal da Bahia, Salvador, Bahia, Zip code: 40.110-040, Brazil; Nuclear Medicine Department, São Rafael Hospital/ Rededor, Salvador, Bahia, Zip code: 41.253-190, Brazil; Nuclearis Corporation, Recife, Pernambuco, Zip code: 50.030-200, Brazil.
| | - Thauan F Moraes
- Northeast Center for Strategic Technologies, Universidade Federal de Pernambuco, Recife, Pernambuco, Zip code: 50.740-545, Brazil
| | - Bruno H L Anjos
- Nuclearis Corporation, Recife, Pernambuco, Zip code: 50.030-200, Brazil
| | - Nadja R G Alencar
- Radiology and Nuclear Medicine Department, Hospital das Clínicas, Federal University of Pernambuco, Recife, Pernambuco, Zip code: 50.670-901, Brazil
| | - Tien-Man C Chang
- Nuclear Medicine Department, Instituto de Medicina Integrada Fernandes Figueira, Recife, Pernambuco, Zip code: 50.070-902, Brazil
| | - Bruno C R F Santana
- Nuclear Medicine Department, São Rafael Hospital/ Rededor, Salvador, Bahia, Zip code: 41.253-190, Brazil
| | - Vinicius O Menezes
- Nuclear Medicine Department, São Rafael Hospital/ Rededor, Salvador, Bahia, Zip code: 41.253-190, Brazil; Nuclearis Corporation, Recife, Pernambuco, Zip code: 50.030-200, Brazil; Radiology and Nuclear Medicine Department, Hospital das Clínicas, Federal University of Pernambuco/ Ebserh, Recife, Pernambuco, Zip code: 50.670-901, Brazil
| | - Lucas O Vieira
- Nuclear Medicine Department, São Rafael Hospital/ Rededor, Salvador, Bahia, Zip code: 41.253-190, Brazil
| | - Simone C S Brandão
- Radiology and Nuclear Medicine Department, Hospital das Clínicas, Federal University of Pernambuco, Recife, Pernambuco, Zip code: 50.670-901, Brazil
| | - Marco A Salvino
- Complexo Hospitalar Universitário Prof. Edgard Santos/ Ebserh, Universidade Federal da Bahia, Salvador, Bahia, Zip code: 40.110-040, Brazil; Hemathology Department, São Rafael Hospital, Salvador, Bahia, Zip code: 41.253-190, Brazil
| | - Eduardo M Netto
- Infectious Disease Research Laboratory, Complexo Hospitalar Universitário Prof. Edgard Santos/ Ebserh, Universidade Federal da Bahia, Salvador, Bahia, Zip code: 40.110-040, Brazil
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Palmas F, Ciudin A, Guerra R, Eiroa D, Espinet C, Roson N, Burgos R, Simó R. Comparison of computed tomography and dual-energy X-ray absorptiometry in the evaluation of body composition in patients with obesity. Front Endocrinol (Lausanne) 2023; 14:1161116. [PMID: 37455915 PMCID: PMC10345841 DOI: 10.3389/fendo.2023.1161116] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Accepted: 04/17/2023] [Indexed: 07/18/2023] Open
Abstract
Objective a) To evaluate the accuracy of the pre-existing equations (based on cm2 provided by CT images), to estimate in kilograms (Kg) the body composition (BC) in patients with obesity (PwO), by comparison with Dual-energy X-ray absorptiometry (DXA). b) To evaluate the accuracy of a new approach (based on both cm2 and Hounsfield Unit parameters provided by CT images), using an automatic software and artificial intelligence to estimate the BC in PwO, by comparison with DXA. Methods Single-centre cross-sectional study including consecutive PwO, matched by gender with subjects with normal BMI. All the subjects underwent BC assessment by Dual-energy X-ray absorptiometry (DXA) and skeletal-CT at L3 vertebrae. CT images were processed using FocusedON-BC software. Three different models were tested. Model 1 and 2, based on the already existing equations, estimate the BC in Kg based on the tissue area (cm2) in the CT images. Model 3, developed in this study, includes as additional variables, the tissue percentage and its average Hounsfield unit. Results 70 subjects (46 PwO and 24 with normal BMI) were recruited. Significant correlations for BC were obtained between the three models and DXA. Model 3 showed the strongest correlation with DXA (r= 0.926, CI95% [0.835-0.968], p<0.001) as well as the best agreement based on Bland - Altman plots. Conclusion This is the first study showing that the BC assessment based on skeletal CT images analyzed by automatic software coupled with artificial intelligence, is accurate in PwO, by comparison with DXA. Furthermore, we propose a new equation that estimates both the tissue quantity and quality, that showed higher accuracy compared with those currently used, both in PwO and subjects with normal BMI.
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Affiliation(s)
- Fiorella Palmas
- Endocrinology and Nutrition Department, Hospital Universitari Vall D´Hebron, Barcelona, Spain
| | - Andreea Ciudin
- Endocrinology and Nutrition Department, Hospital Universitari Vall D´Hebron, Barcelona, Spain
- Diabetes and Metabolism Research Unit, Vall d’Hebron Institut De Recerca (VHIR), Barcelona, Spain
- Department of Medicine, Universitat Autònoma De Barcelona, Barcelona, Spain
- Centro De Investigación Biomédica En Red De Diabetes y Enfermedades Metabólicas Asociadas (CIBERDEM), Instituto De Salud Carlos III (ISCIII), Madrid, Spain
| | | | - Daniel Eiroa
- Department of Radiology, Institut De Diagnòstic Per La Imatge (IDI), Hospital Universitari Vall d’Hebron, Barcelona, Spain
| | - Carina Espinet
- Nuclear Medicine Deparment, Vall Hebron Hospital, Barcelona, Spain
| | - Nuria Roson
- Department of Radiology, Institut De Diagnòstic Per La Imatge (IDI), Hospital Universitari Vall d’Hebron, Barcelona, Spain
| | - Rosa Burgos
- Endocrinology and Nutrition Department, Hospital Universitari Vall D´Hebron, Barcelona, Spain
- Diabetes and Metabolism Research Unit, Vall d’Hebron Institut De Recerca (VHIR), Barcelona, Spain
- Department of Medicine, Universitat Autònoma De Barcelona, Barcelona, Spain
| | - Rafael Simó
- Endocrinology and Nutrition Department, Hospital Universitari Vall D´Hebron, Barcelona, Spain
- Diabetes and Metabolism Research Unit, Vall d’Hebron Institut De Recerca (VHIR), Barcelona, Spain
- Department of Medicine, Universitat Autònoma De Barcelona, Barcelona, Spain
- Centro De Investigación Biomédica En Red De Diabetes y Enfermedades Metabólicas Asociadas (CIBERDEM), Instituto De Salud Carlos III (ISCIII), Madrid, Spain
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Hong JH, Hong H, Choi YR, Kim DH, Kim JY, Yoon JH, Yoon SH. CT analysis of thoracolumbar body composition for estimating whole-body composition. Insights Imaging 2023; 14:69. [PMID: 37093330 PMCID: PMC10126176 DOI: 10.1186/s13244-023-01402-z] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2022] [Accepted: 03/11/2023] [Indexed: 04/25/2023] Open
Abstract
BACKGROUND To evaluate the correlation between single- and multi-slice cross-sectional thoracolumbar and whole-body compositions. METHODS We retrospectively included patients who underwent whole-body PET-CT scans from January 2016 to December 2019 at multiple institutions. A priori-developed, deep learning-based commercially available 3D U-Net segmentation provided whole-body 3D reference volumes and 2D areas of muscle, visceral fat, and subcutaneous fat at the upper, middle, and lower endplate of the individual T1-L5 vertebrae. In the derivation set, we analyzed the Pearson correlation coefficients of single-slice and multi-slice averaged 2D areas (waist and T12-L1) with the reference values. We then built prediction models using the top three correlated levels and tested the models in the validation set. RESULTS The derivation and validation datasets included 203 (mean age 58.2 years; 101 men) and 239 patients (mean age 57.8 years; 80 men). The coefficients were distributed bimodally, with the first peak at T4 (coefficient, 0.78) and the second peak at L2-3 (coefficient 0.90). The top three correlations in the abdominal scan range were found for multi-slice waist averaging (0.92) and single-slice L3 and L2 (0.90, each), while those in the chest scan range were multi-slice T12-L1 averaging (0.89), single-slice L1 (0.89), and T12 (0.86). The model performance at the top three levels for estimating whole-body composition was similar in the derivation and validation datasets. CONCLUSIONS Single-slice L2-3 (abdominal CT range) and L1 (chest CT range) analysis best correlated with whole-body composition around 0.90 (coefficient). Multi-slice waist averaging provided a slightly higher correlation of 0.92.
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Affiliation(s)
- Jung Hee Hong
- Department of Radiology, Dongsan Hospital, Keimyung University College of Medicine, Daegu, Korea
| | - Hyunsook Hong
- Medical Research Collaborating Center, Seoul National University Hospital, Seoul, Korea
| | - Ye Ra Choi
- Department of Radiology, Seoul Metropolitan Government-Seoul National University Boramae Medical Center, Seoul, Korea
| | - Dong Hyun Kim
- Department of Radiology, Seoul Metropolitan Government-Seoul National University Boramae Medical Center, Seoul, Korea
| | - Jin Young Kim
- Department of Radiology, Dongsan Hospital, Keimyung University College of Medicine, Daegu, Korea
| | - Jeong-Hwa Yoon
- Institute of Health Policy and Management, Medical Research Center, Seoul National University, Seoul, Korea
| | - Soon Ho Yoon
- Department of Radiology, Seoul National University College of Medicine, Seoul National University Hospital, 101 Daehak-ro, Chongno-gu, Seoul, 03080, Republic of Korea.
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Santhanam P, Nath T, Peng C, Bai H, Zhang H, Ahima RS, Chellappa R. Artificial intelligence and body composition. Diabetes Metab Syndr 2023; 17:102732. [PMID: 36867973 DOI: 10.1016/j.dsx.2023.102732] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/25/2022] [Revised: 02/18/2023] [Accepted: 02/20/2023] [Indexed: 02/27/2023]
Abstract
AIMS Although obesity is associated with chronic disease, a large section of the population with high BMI does not have an increased risk of metabolic disease. Increased visceral adiposity and sarcopenia are also risk factors for metabolic disease in people with normal BMI. Artificial Intelligence (AI) techniques can help assess and analyze body composition parameters for predicting cardiometabolic health. The purpose of the study was to systematically explore literature involving AI techniques for body composition assessment and observe general trends. METHODS We searched the following databases: Embase, Web of Science, and PubMed. There was a total of 354 search results. After removing duplicates, irrelevant studies, and reviews(a total of 303), 51 studies were included in the systematic review. RESULTS AI techniques have been studied for body composition analysis in the context of diabetes mellitus, hypertension, cancer and many specialized diseases. Imaging techniques employed for AI methods include CT (Computerized Tomography), MRI (Magnetic Resonance Imaging), ultrasonography, plethysmography, and EKG(Electrocardiogram). Automatic segmentation of body composition by deep learning with convolutional networks has helped determine and quantify muscle mass. Limitations include heterogeneity of study populations, inherent bias in sampling, and lack of generalizability. Different bias mitigation strategies should be evaluated to address these problems and improve the applicability of AI to body composition analysis. CONCLUSIONS AI assisted measurement of body composition might assist in improved cardiovascular risk stratification when applied in the appropriate clinical context.
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Affiliation(s)
- Prasanna Santhanam
- Division of Endocrinology, Diabetes, & Metabolism, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, 21287, USA.
| | - Tanmay Nath
- Department Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, 21287, USA
| | - Cheng Peng
- Department of Electrical and Computer Engineering, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, 21287, USA
| | - Harrison Bai
- Department of Radiology and Radiology Sciences, Johns Hopkins University, Baltimore, MD, 21287, USA
| | - Helen Zhang
- The Warren Alpert Medical School of Brown University, Providence, RI, 02903, USA
| | - Rexford S Ahima
- Division of Endocrinology, Diabetes, & Metabolism, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, 21287, USA
| | - Rama Chellappa
- Department of Electrical and Computer Engineering, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, 21287, USA; Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, 21287, USA
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