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Rau A, Reisert M, Stein T, Mueller-Peltzer K, Rau S, Bamberg F, Taschner CA, Urbach H, Kellner E. Impact of temporal resolution on perfusion metrics, therapy decision, and radiation dose reduction in brain CT perfusion in patients with suspected stroke. Neuroradiology 2024; 66:749-759. [PMID: 38498208 PMCID: PMC11031466 DOI: 10.1007/s00234-024-03335-w] [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: 12/01/2023] [Accepted: 03/11/2024] [Indexed: 03/20/2024]
Abstract
PURPOSE CT perfusion of the brain is a powerful tool in stroke imaging, though the radiation dose is rather high. Several strategies for dose reduction have been proposed, including increasing the intervals between the dynamic scans. We determined the impact of temporal resolution on perfusion metrics, therapy decision, and radiation dose reduction in brain CT perfusion from a large dataset of patients with suspected stroke. METHODS We retrospectively included 3555 perfusion scans from our clinical routine dataset. All cases were processed using the perfusion software VEOcore with a standard sampling of 1.5 s, as well as simulated reduced temporal resolution of 3.0, 4.5, and 6.0 s by leaving out respective time points. The resulting perfusion maps and calculated volumes of infarct core and mismatch were compared quantitatively. Finally, hypothetical decisions for mechanical thrombectomy following the DEFUSE-3 criteria were compared. RESULTS The agreement between calculated volumes for core (ICC = 0.99, 0.99, and 0.98) and hypoperfusion (ICC = 0.99, 0.99, and 0.97) was excellent for all temporal sampling schemes. Of the 1226 cases with vascular occlusion, 14 (1%) for 3.0 s sampling, 23 (2%) for 4.5 s sampling, and 63 (5%) for 6.0 s sampling would have been treated differently if the DEFUSE-3 criteria had been applied. Reduction of temporal resolution to 3.0 s, 4.5 s, and 6.0 s reduced the radiation dose by a factor of 2, 3, or 4. CONCLUSION Reducing the temporal sampling of brain perfusion CT has only a minor impact on image quality and treatment decision, but significantly reduces the radiation dose to that of standard non-contrast CT.
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Affiliation(s)
- Alexander Rau
- Department of Neurology and Clinical Neuroscience, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany.
- Department of Diagnostic and Interventional Radiology, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany.
| | - Marco Reisert
- Department of Medical Physics, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
- Department of Stereotactic and Functional Neurosurgery, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Thomas Stein
- Department of Diagnostic and Interventional Radiology, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Katharina Mueller-Peltzer
- Department of Diagnostic and Interventional Radiology, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Stephan Rau
- Department of Diagnostic and Interventional Radiology, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Fabian Bamberg
- Department of Diagnostic and Interventional Radiology, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Christian A Taschner
- Department of Neurology and Clinical Neuroscience, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Horst Urbach
- Department of Neurology and Clinical Neuroscience, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Elias Kellner
- Department of Medical Physics, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
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Ioannidis GS, Pigott LE, Iv M, Surlan-Popovic K, Wintermark M, Bisdas S, Marias K. Investigating the value of radiomics stemming from DSC quantitative biomarkers in IDH mutation prediction in gliomas. Front Neurol 2023; 14:1249452. [PMID: 38046592 PMCID: PMC10690367 DOI: 10.3389/fneur.2023.1249452] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2023] [Accepted: 10/31/2023] [Indexed: 12/05/2023] Open
Abstract
Objective This study aims to assess the value of biomarker based radiomics to predict IDH mutation in gliomas. The patient cohort consists of 160 patients histopathologicaly proven of primary glioma (WHO grades 2-4) from 3 different centers. Methods To quantify the DSC perfusion signal two different mathematical modeling methods were used (Gamma fitting, leakage correction algorithms) considering the assumptions about the compartments contributing in the blood flow between the extra- and intra vascular space. Results The Mean slope of increase (MSI) and the K1 parameter of the bidirectional exchange model exhibited the highest performance with (ACC 74.3% AUROC 74.2%) and (ACC 75% AUROC 70.5%) respectively. Conclusion The proposed framework on DSC-MRI radiogenomics in gliomas has the potential of becoming a reliable diagnostic support tool exploiting the mathematical modeling of the DSC signal to characterize IDH mutation status through a more reproducible and standardized signal analysis scheme for facilitating clinical translation.
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Affiliation(s)
- Georgios S. Ioannidis
- Computational BioMedicine Laboratory (CBML), Institute of Computer Science, Foundation for Research and Technology—Hellas (FORTH), Heraklion, Greece
| | - Laura Elin Pigott
- Institute of Health and Social Care, London South Bank University, London, United Kingdom
- Faculty of Brain Science, Queen Square Institute of Neurology, University College London, London, United Kingdom
- Lysholm Department of Neuroradiology, The National Hospital for Neurology and Neurosurgery University College London, London, United Kingdom
| | - Michael Iv
- Department of Radiology, Division of Neuroimaging and Neurointervention, Stanford University, Stanford, CA, United States
| | - Katarina Surlan-Popovic
- Department of Radiology, Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
- Department of Neuroradiology, University Medical Centre, Ljubljana, Slovenia
| | - Max Wintermark
- Department of Radiology, Division of Neuroimaging and Neurointervention, Stanford University, Stanford, CA, United States
| | - Sotirios Bisdas
- Department of Brain Repair and Rehabilitation, Queen Square Institute of Neurology, UCL, London, United Kingdom
- Department of Neuroradiology, The National Hospital for Neurology and Neurosurgery, University College London NHS Foundation Trust, London, United Kingdom
| | - Kostas Marias
- Computational BioMedicine Laboratory (CBML), Institute of Computer Science, Foundation for Research and Technology—Hellas (FORTH), Heraklion, Greece
- Department of Electrical and Computer Engineering, Hellenic Mediterranean University, Heraklion, Greece
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Ma G, Cao YZ, Shen GC, Lu SS, Zhang YX, Zhang Y, Shi HB, Xu XQ, Wu FY. CT perfusion with increased temporal sampling interval to predict target mismatch status in patients with acute ischemic stroke. Neuroradiology 2023; 65:105-111. [PMID: 35925438 DOI: 10.1007/s00234-022-03026-4] [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: 05/11/2022] [Accepted: 07/22/2022] [Indexed: 01/12/2023]
Abstract
PURPOSE To evaluate the feasibility of using CT perfusion (CTP) with increased temporal sampling interval to predict the target mismatch status in acute ischemic stroke (AIS) patients with anterior circular large-vessel occlusion (LVO). METHODS CTP with a sampling interval of 1.7 s (CTP1.7 s) was scanned in 77 AIS patients for pre-treatment evaluation. Simulated CTP data with sampling interval of 3.4 s (CTP3.4 s) or 5.1 s (CTP5.1 s) were reconstructed, respectively. Target mismatch was defined according to the EXTEND-IA (Extending the Time for Thrombolysis in Emergency Neurological Deficits-Intra-Arterial) and DEFUSE 3 (Endovascular Therapy Following Imaging Evaluation for Ischemic Stroke) trial criteria, respectively. Pearson correlation analysis, Mann-Whitney U test, Bland-Altman analysis, and chi-square test were used for statistical analysis as appropriate. RESULTS Significant correlations were found on the volume of ischemic core, hypo-perfused area, mismatch area, and ratio between CTP1.7 s and CTP3.4 s or CTP5.1 s (all p < 0.001). There was no significant difference on the volume of ischemic core, hypo-perfused area, mismatch area, and mismatch ratio between CTP1.7 s and CTP3.4 s or CTP5.1 s (all p > 0.05). Compared with CTP1.7 s, CTP3.4 s or CTP5.1 s showed comparable performance in predicting the target mismatch status in the AIS patients with LVO (both p > 0.05). CONCLUSIONS CTPs with increased temporal sampling intervals that lead to reduced radiation doses are feasible and may provide comparable performance in predicting target mismatch status in AIS patients with LVO.
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Affiliation(s)
- Gao Ma
- Department of Radiology, the First Affiliated Hospital of Nanjing Medical University, No. 300, Guangzhou Rd., Nanjing, China
| | - Yue-Zhou Cao
- Department of Interventional Radiology, the First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Guang-Chen Shen
- Department of Radiology, the First Affiliated Hospital of Nanjing Medical University, No. 300, Guangzhou Rd., Nanjing, China
| | - Shan-Shan Lu
- Department of Radiology, the First Affiliated Hospital of Nanjing Medical University, No. 300, Guangzhou Rd., Nanjing, China
| | - Ya-Xi Zhang
- Shukun Network Technology Co., Ltd, Beijing, China
| | - Yu Zhang
- Shukun Network Technology Co., Ltd, Beijing, China
| | - Hai-Bin Shi
- Department of Interventional Radiology, the First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Xiao-Quan Xu
- Department of Radiology, the First Affiliated Hospital of Nanjing Medical University, No. 300, Guangzhou Rd., Nanjing, China.
| | - Fei-Yun Wu
- Department of Radiology, the First Affiliated Hospital of Nanjing Medical University, No. 300, Guangzhou Rd., Nanjing, China.
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Zeng D, Zeng C, Zeng Z, Li S, Deng Z, Chen S, Bian Z, Ma J. Basis and current state of computed tomography perfusion imaging: a review. Phys Med Biol 2022; 67. [PMID: 35926503 DOI: 10.1088/1361-6560/ac8717] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2021] [Accepted: 08/04/2022] [Indexed: 12/30/2022]
Abstract
Computed tomography perfusion (CTP) is a functional imaging that allows for providing capillary-level hemodynamics information of the desired tissue in clinics. In this paper, we aim to offer insight into CTP imaging which covers the basics and current state of CTP imaging, then summarize the technical applications in the CTP imaging as well as the future technological potential. At first, we focus on the fundamentals of CTP imaging including systematically summarized CTP image acquisition and hemodynamic parameter map estimation techniques. A short assessment is presented to outline the clinical applications with CTP imaging, and then a review of radiation dose effect of the CTP imaging on the different applications is presented. We present a categorized methodology review on known and potential solvable challenges of radiation dose reduction in CTP imaging. To evaluate the quality of CTP images, we list various standardized performance metrics. Moreover, we present a review on the determination of infarct and penumbra. Finally, we reveal the popularity and future trend of CTP imaging.
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Affiliation(s)
- Dong Zeng
- School of Biomedical Engineering, Southern Medical University, Guangdong 510515, China; and Guangzhou Key Laboratory of Medical Radiation Imaging and Detection Technology, Southern Medical University, Guangdong 510515, People's Republic of China
| | - Cuidie Zeng
- School of Biomedical Engineering, Southern Medical University, Guangdong 510515, China; and Guangzhou Key Laboratory of Medical Radiation Imaging and Detection Technology, Southern Medical University, Guangdong 510515, People's Republic of China
| | - Zhixiong Zeng
- School of Biomedical Engineering, Southern Medical University, Guangdong 510515, China; and Guangzhou Key Laboratory of Medical Radiation Imaging and Detection Technology, Southern Medical University, Guangdong 510515, People's Republic of China
| | - Sui Li
- School of Biomedical Engineering, Southern Medical University, Guangdong 510515, China; and Guangzhou Key Laboratory of Medical Radiation Imaging and Detection Technology, Southern Medical University, Guangdong 510515, People's Republic of China
| | - Zhen Deng
- Department of Neurology, Nanfang Hospital, Southern Medical University, Guangdong 510515, People's Republic of China
| | - Sijin Chen
- Department of Medical Imaging Center, Nanfang Hospital, Southern Medical University, Guangdong 510515, People's Republic of China
| | - Zhaoying Bian
- School of Biomedical Engineering, Southern Medical University, Guangdong 510515, China; and Guangzhou Key Laboratory of Medical Radiation Imaging and Detection Technology, Southern Medical University, Guangdong 510515, People's Republic of China
| | - Jianhua Ma
- School of Biomedical Engineering, Southern Medical University, Guangdong 510515, China; and Guangzhou Key Laboratory of Medical Radiation Imaging and Detection Technology, Southern Medical University, Guangdong 510515, People's Republic of China
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Quantification and Classification of Contrast Enhanced Ultrasound Breast Cancer Data: A Preliminary Study. Diagnostics (Basel) 2022; 12:diagnostics12020425. [PMID: 35204514 PMCID: PMC8871488 DOI: 10.3390/diagnostics12020425] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2021] [Revised: 02/02/2022] [Accepted: 02/03/2022] [Indexed: 02/01/2023] Open
Abstract
This study aimed to investigate which of the two frequently adopted perfusion models better describes the contrast enhanced ultrasound (CEUS) perfusion signal in order to produce meaningful imaging markers with the goal of developing a machine-learning model that can classify perfusion curves as benign or malignant in breast cancer data. Twenty-five patients with high suspicion of breast cancer were analyzed with exponentially modified Gaussian (EMG) and gamma variate functions (GVF). The adjusted R2 metric was the criterion for assessing model performance. Various classifiers were trained on the quantified perfusion curves in order to classify the curves as benign or malignant on a voxel basis. Sensitivity, specificity, geometric mean, and AUROC were the validation metrics. The best quantification model was EMG with an adjusted R2 of 0.60 ± 0.26 compared to 0.56 ± 0.25 for GVF. Logistic regression was the classifier with the highest performance (sensitivity, specificity, Gmean, and AUROC = 89.2 ± 10.7, 70.0 ± 18.5, 77.1 ± 8.6, and 91.0 ± 6.6, respectively). This classification method obtained similar results that are consistent with the current literature. Breast cancer patients can benefit from early detection and characterization prior to biopsy.
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Granata V, Coppola F, Grassi R, Fusco R, Tafuto S, Izzo F, Reginelli A, Maggialetti N, Buccicardi D, Frittoli B, Rengo M, Bortolotto C, Prost R, Lacasella GV, Montella M, Ciaghi E, Bellifemine F, De Muzio F, Danti G, Grazzini G, De Filippo M, Cappabianca S, Barresi C, Iafrate F, Stoppino LP, Laghi A, Grassi R, Brunese L, Neri E, Miele V, Faggioni L. Structured Reporting of Computed Tomography in the Staging of Neuroendocrine Neoplasms: A Delphi Consensus Proposal. Front Endocrinol (Lausanne) 2021; 12:748944. [PMID: 34917023 PMCID: PMC8670531 DOI: 10.3389/fendo.2021.748944] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/28/2021] [Accepted: 11/12/2021] [Indexed: 12/29/2022] Open
Abstract
BACKGROUND Structured reporting (SR) in radiology is becoming increasingly necessary and has been recognized recently by major scientific societies. This study aims to build structured CT-based reports in Neuroendocrine Neoplasms during the staging phase in order to improve communication between the radiologist and members of multidisciplinary teams. MATERIALS AND METHODS A panel of expert radiologists, members of the Italian Society of Medical and Interventional Radiology, was established. A Modified Delphi process was used to develop the SR and to assess a level of agreement for all report sections. Cronbach's alpha (Cα) correlation coefficient was used to assess internal consistency for each section and to measure quality analysis according to the average inter-item correlation. RESULTS The final SR version was built by including n=16 items in the "Patient Clinical Data" section, n=13 items in the "Clinical Evaluation" section, n=8 items in the "Imaging Protocol" section, and n=17 items in the "Report" section. Overall, 54 items were included in the final version of the SR. Both in the first and second round, all sections received more than a good rating: a mean value of 4.7 and range of 4.2-5.0 in the first round and a mean value 4.9 and range of 4.9-5 in the second round. In the first round, the Cα correlation coefficient was a poor 0.57: the overall mean score of the experts and the sum of scores for the structured report were 4.7 (range 1-5) and 728 (mean value 52.00 and standard deviation 2.83), respectively. In the second round, the Cα correlation coefficient was a good 0.82: the overall mean score of the experts and the sum of scores for the structured report were 4.9 (range 4-5) and 760 (mean value 54.29 and standard deviation 1.64), respectively. CONCLUSIONS The present SR, based on a multi-round consensus-building Delphi exercise following in-depth discussion between expert radiologists in gastro-enteric and oncological imaging, derived from a multidisciplinary agreement between a radiologist, medical oncologist and surgeon in order to obtain the most appropriate communication tool for referring physicians.
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Affiliation(s)
- Vincenza Granata
- Division of Radiology, “Istituto Nazionale Tumori IRCCS Fondazione Pascale – IRCCS di Napoli”, Naples, Italy
| | - Francesca Coppola
- Department of Radiology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy
| | - Roberta Grassi
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, Milan, Italy
- Division of Radiology, “Università degli Studi della Campania Luigi Vanvitelli”, Naples, Italy
| | | | - Salvatore Tafuto
- Medical Oncology Unit, Istituto Nazionale Tumori IRCCS ‘Fondazione G. Pascale’, Naples, Italy
| | - Francesco Izzo
- Department of Surgery, Istituto Nazionale Tumori -IRCCS- Fondazione G. Pascale, Naples, Italy
| | - Alfonso Reginelli
- Division of Radiology, “Università degli Studi della Campania Luigi Vanvitelli”, Naples, Italy
| | | | | | - Barbara Frittoli
- Department of Radiology, Ospedali Civili, Hospital of Brescia, University of Brescia, Brescia, Italy
| | - Marco Rengo
- Department of Radiological Sciences, Oncology and Pathology, Sapienza University of Rome - I.C.O.T. Hospital, Latina, Italy
| | - Chandra Bortolotto
- Department of Radiology, I.R.C.C.S. Policlinico San Matteo Foundation, Pavia, Italy
| | - Roberto Prost
- Radiology Unit, Azienda Ospedaliera Brotzu, Cagliari, Italy
| | - Giorgia Viola Lacasella
- Division of Radiology, “Università degli Studi della Campania Luigi Vanvitelli”, Naples, Italy
| | - Marco Montella
- Division of Radiology, “Università degli Studi della Campania Luigi Vanvitelli”, Naples, Italy
| | | | | | - Federica De Muzio
- Department of Medicine and Health Sciences “V. Tiberio”, University of Molise, Campobasso, Italy
| | - Ginevra Danti
- Division of Radiology, “Azienda Ospedaliera Universitaria Careggi”, Florence, Italy
- *Correspondence: Ginevra Danti,
| | - Giulia Grazzini
- Division of Radiology, “Azienda Ospedaliera Universitaria Careggi”, Florence, Italy
| | - Massimo De Filippo
- Department of Medicine and Surgery, Unit of Radiology, University of Parma, Maggiore Hospital, Parma, Italy
| | - Salvatore Cappabianca
- Division of Radiology, “Università degli Studi della Campania Luigi Vanvitelli”, Naples, Italy
| | - Carmelo Barresi
- Diagnostic Imaging Section, Department of Medical and Surgical Sciences & Neurosciences, Siena University Hospital, Siena, Italy
| | - Franco Iafrate
- Department of Radiological, Oncological and Pathological Sciences, Policlinico Umberto I, Sapienza University of Rome, Rome, Italy
| | | | - Andrea Laghi
- Department of Surgical and Medical Sciences and Translational Medicine, Sapienza University of Rome-Sant’Andrea University Hospital, Rome, Italy
| | - Roberto Grassi
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, Milan, Italy
- Division of Radiology, “Università degli Studi della Campania Luigi Vanvitelli”, Naples, Italy
| | - Luca Brunese
- Department of Medicine and Health Sciences “V. Tiberio”, University of Molise, Campobasso, Italy
| | - Emanuele Neri
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, Milan, Italy
- Department of Translational Research, University of Pisa, Pisa, Italy
| | - Vittorio Miele
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, Milan, Italy
- Division of Radiology, “Azienda Ospedaliera Universitaria Careggi”, Florence, Italy
| | - Lorenzo Faggioni
- Department of Translational Research, University of Pisa, Pisa, Italy
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