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Wang L, Chen X, Zhang L, Li L, Huang Y, Sun Y, Yuan X. Artificial intelligence in clinical decision support systems for oncology. Int J Med Sci 2023; 20:79-86. [PMID: 36619220 PMCID: PMC9812798 DOI: 10.7150/ijms.77205] [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] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/17/2022] [Accepted: 12/01/2022] [Indexed: 12/23/2022] Open
Abstract
Artificial intelligence (AI) has been widely used in various medical fields, such as image diagnosis, pathological classification, selection of treatment schemes, and prognosis analysis. Especially in the image-aided diagnosis of tumors, the cooperation of human-computer interactions has become mature. However, the ethics of the application of AI as an emerging technology in clinical decision-making have not been fully supported, so the clinical decision support system (CDSS) based on AI technology has not fully realized human-computer interactions in clinical practice as the image-aided diagnosis system. The CDSS was currently used and promoted worldwide including Watson for Oncology, Chinese society of clinical oncology-artificial intelligence (CSCO AI) and so on. This paper summarized the applications and clarified the principle of AI in CDSS, analyzed the difficulties of AI in oncology decisions, and provided a reference scheme for the application of AI in oncology decisions in the future.
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Affiliation(s)
- Lu Wang
- Department of Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei Province, China
| | - Xinyi Chen
- Department of Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei Province, China
| | - Lu Zhang
- Department of Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei Province, China
| | - Long Li
- Department of Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei Province, China
| | - YongBiao Huang
- Department of Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei Province, China
| | - Yinan Sun
- Department of Cardiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei Province, China
| | - Xianglin Yuan
- Department of Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei Province, China
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Zhou X, Yue X, Xu Z, Denoeux T, Chen Y. PENet: Prior Evidence Deep Neural Network for Bladder Cancer Staging. Methods 2022; 207:20-28. [PMID: 36031139 DOI: 10.1016/j.ymeth.2022.08.010] [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: 03/20/2022] [Revised: 08/10/2022] [Accepted: 08/21/2022] [Indexed: 10/31/2022] Open
Abstract
Bladder cancer is a heterogeneous, complicated, and widespread illness with high rates of morbidity, death, and expense if not treated adequately. The accurate and exact stage of bladder cancer is fundamental for treatment choices and prognostic forecasts, as indicated by convincing evidence from randomized trials. The extraordinary capability of Deep Convolutional Neural Networks (DCNNs) to extract features is one of the primary advantages offered by these types of networks. DCNNs work well in numerous real clinical medical applications as it demands costly large-scale data annotation. However, a lack of background information hinders its effectiveness and interpretability. Clinicians identify the stage of a tumor by evaluating whether the tumor is muscle-invasive, as shown in images by the tumor's infiltration of the bladder wall. Incorporating this clinical knowledge in DCNN has the ability to enhance the performance of bladder cancer staging and bring the prediction into accordance with medical principles. Therefore, we introduce PENet, innovative prior evidence deep neural network, for classifying MR images of bladder cancer staging in line with clinical knowledge. To do this, first, the degree to which the tumor has penetrated into the bladder wall is measured to get prior distribution parameters of class probability called prior evidence. Second, we formulate the posterior distribution of class probability according to Bayesian Theorem. Last, we modify the loss function based on posterior distribution of class probability which parameters include both prior evidence and prediction evidence in the learning procedure. Our investigation reveals that the prediction error and the variance of PENet may be reduced by giving the network prior evidence that is consistent with the ground truth. Using MR image datasets, experiments show that PENet performs better than image-based DCNN algorithms for bladder cancer staging.
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Affiliation(s)
- Xiaoqian Zhou
- School of Computer Engineering and Science, Shanghai University, Shanghai, China.
| | - Xiaodong Yue
- School of Computer Engineering and Science, Shanghai University, Shanghai, China; Artificial Intelligence Institute of Shanghai University, Shanghai, China.
| | - Zhikang Xu
- School of Computer Engineering and Science, Shanghai University, Shanghai, China.
| | - Thierry Denoeux
- Sino-European School of Technology, Shanghai University, Shanghai, China; Université de technologie de Compiégne, Compiégne, France.
| | - Yufei Chen
- College of Electronics and Information Engineering, Tongji University, Shanghai, China.
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Sun D, Hadjiiski L, Alva A, Zakharia Y, Joshi M, Chan HP, Garje R, Pomerantz L, Elhag D, Cohan RH, Caoili EM, Kerr WT, Cha KH, Kirova-Nedyalkova G, Davenport MS, Shankar PR, Francis IR, Shampain K, Meyer N, Barkmeier D, Woolen S, Palmbos PL, Weizer AZ, Samala RK, Zhou C, Matuszak M. Computerized Decision Support for Bladder Cancer Treatment Response Assessment in CT Urography: Effect on Diagnostic Accuracy in Multi-Institution Multi-Specialty Study. Tomography 2022; 8:644-656. [PMID: 35314631 PMCID: PMC8938803 DOI: 10.3390/tomography8020054] [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] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2022] [Revised: 02/17/2022] [Accepted: 02/28/2022] [Indexed: 11/22/2022] Open
Abstract
This observer study investigates the effect of computerized artificial intelligence (AI)-based decision support system (CDSS-T) on physicians’ diagnostic accuracy in assessing bladder cancer treatment response. The performance of 17 observers was evaluated when assessing bladder cancer treatment response without and with CDSS-T using pre- and post-chemotherapy CTU scans in 123 patients having 157 pre- and post-treatment cancer pairs. The impact of cancer case difficulty, observers’ clinical experience, institution affiliation, specialty, and the assessment times on the observers’ diagnostic performance with and without using CDSS-T were analyzed. It was found that the average performance of the 17 observers was significantly improved (p = 0.002) when aided by the CDSS-T. The cancer case difficulty, institution affiliation, specialty, and the assessment times influenced the observers’ performance without CDSS-T. The AI-based decision support system has the potential to improve the diagnostic accuracy in assessing bladder cancer treatment response and result in more consistent performance among all physicians.
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Affiliation(s)
- Di Sun
- Department of Radiology, University of Michigan, Ann Arbor, MI 48109, USA; (L.H.); (H.-P.C.); (R.H.C.); (E.M.C.); (M.S.D.); (P.R.S.); (I.R.F.); (K.S.); (N.M.); (D.B.); (S.W.); (R.K.S.); (C.Z.)
- Correspondence:
| | - Lubomir Hadjiiski
- Department of Radiology, University of Michigan, Ann Arbor, MI 48109, USA; (L.H.); (H.-P.C.); (R.H.C.); (E.M.C.); (M.S.D.); (P.R.S.); (I.R.F.); (K.S.); (N.M.); (D.B.); (S.W.); (R.K.S.); (C.Z.)
| | - Ajjai Alva
- Department of Internal Medicine-Hematology/Oncology, University of Michigan, Ann Arbor, MI 48109, USA; (A.A.); (P.L.P.)
| | - Yousef Zakharia
- Department of Internal Medicine-Hematology/Oncology, University of Iowa, Iowa, IA 52242, USA; (Y.Z.); (R.G.); (D.E.)
| | - Monika Joshi
- Department of Internal Medicine-Hematology/Oncology, Pennsylvania State University, Hershey, PA 16801, USA; (M.J.); (L.P.)
| | - Heang-Ping Chan
- Department of Radiology, University of Michigan, Ann Arbor, MI 48109, USA; (L.H.); (H.-P.C.); (R.H.C.); (E.M.C.); (M.S.D.); (P.R.S.); (I.R.F.); (K.S.); (N.M.); (D.B.); (S.W.); (R.K.S.); (C.Z.)
| | - Rohan Garje
- Department of Internal Medicine-Hematology/Oncology, University of Iowa, Iowa, IA 52242, USA; (Y.Z.); (R.G.); (D.E.)
| | - Lauren Pomerantz
- Department of Internal Medicine-Hematology/Oncology, Pennsylvania State University, Hershey, PA 16801, USA; (M.J.); (L.P.)
| | - Dean Elhag
- Department of Internal Medicine-Hematology/Oncology, University of Iowa, Iowa, IA 52242, USA; (Y.Z.); (R.G.); (D.E.)
| | - Richard H. Cohan
- Department of Radiology, University of Michigan, Ann Arbor, MI 48109, USA; (L.H.); (H.-P.C.); (R.H.C.); (E.M.C.); (M.S.D.); (P.R.S.); (I.R.F.); (K.S.); (N.M.); (D.B.); (S.W.); (R.K.S.); (C.Z.)
| | - Elaine M. Caoili
- Department of Radiology, University of Michigan, Ann Arbor, MI 48109, USA; (L.H.); (H.-P.C.); (R.H.C.); (E.M.C.); (M.S.D.); (P.R.S.); (I.R.F.); (K.S.); (N.M.); (D.B.); (S.W.); (R.K.S.); (C.Z.)
| | - Wesley T. Kerr
- Department of Neurology, University of Michigan, Ann Arbor, MI 48109, USA;
| | - Kenny H. Cha
- U.S. Food and Drug Administration, Center for Devices and Radiological Health, Silver Spring, MD 20993, USA;
| | | | - Matthew S. Davenport
- Department of Radiology, University of Michigan, Ann Arbor, MI 48109, USA; (L.H.); (H.-P.C.); (R.H.C.); (E.M.C.); (M.S.D.); (P.R.S.); (I.R.F.); (K.S.); (N.M.); (D.B.); (S.W.); (R.K.S.); (C.Z.)
- Department of Urology, University of Michigan, Ann Arbor, MI 48109, USA;
| | - Prasad R. Shankar
- Department of Radiology, University of Michigan, Ann Arbor, MI 48109, USA; (L.H.); (H.-P.C.); (R.H.C.); (E.M.C.); (M.S.D.); (P.R.S.); (I.R.F.); (K.S.); (N.M.); (D.B.); (S.W.); (R.K.S.); (C.Z.)
| | - Isaac R. Francis
- Department of Radiology, University of Michigan, Ann Arbor, MI 48109, USA; (L.H.); (H.-P.C.); (R.H.C.); (E.M.C.); (M.S.D.); (P.R.S.); (I.R.F.); (K.S.); (N.M.); (D.B.); (S.W.); (R.K.S.); (C.Z.)
| | - Kimberly Shampain
- Department of Radiology, University of Michigan, Ann Arbor, MI 48109, USA; (L.H.); (H.-P.C.); (R.H.C.); (E.M.C.); (M.S.D.); (P.R.S.); (I.R.F.); (K.S.); (N.M.); (D.B.); (S.W.); (R.K.S.); (C.Z.)
| | - Nathaniel Meyer
- Department of Radiology, University of Michigan, Ann Arbor, MI 48109, USA; (L.H.); (H.-P.C.); (R.H.C.); (E.M.C.); (M.S.D.); (P.R.S.); (I.R.F.); (K.S.); (N.M.); (D.B.); (S.W.); (R.K.S.); (C.Z.)
| | - Daniel Barkmeier
- Department of Radiology, University of Michigan, Ann Arbor, MI 48109, USA; (L.H.); (H.-P.C.); (R.H.C.); (E.M.C.); (M.S.D.); (P.R.S.); (I.R.F.); (K.S.); (N.M.); (D.B.); (S.W.); (R.K.S.); (C.Z.)
| | - Sean Woolen
- Department of Radiology, University of Michigan, Ann Arbor, MI 48109, USA; (L.H.); (H.-P.C.); (R.H.C.); (E.M.C.); (M.S.D.); (P.R.S.); (I.R.F.); (K.S.); (N.M.); (D.B.); (S.W.); (R.K.S.); (C.Z.)
| | - Phillip L. Palmbos
- Department of Internal Medicine-Hematology/Oncology, University of Michigan, Ann Arbor, MI 48109, USA; (A.A.); (P.L.P.)
| | - Alon Z. Weizer
- Department of Urology, University of Michigan, Ann Arbor, MI 48109, USA;
| | - Ravi K. Samala
- Department of Radiology, University of Michigan, Ann Arbor, MI 48109, USA; (L.H.); (H.-P.C.); (R.H.C.); (E.M.C.); (M.S.D.); (P.R.S.); (I.R.F.); (K.S.); (N.M.); (D.B.); (S.W.); (R.K.S.); (C.Z.)
| | - Chuan Zhou
- Department of Radiology, University of Michigan, Ann Arbor, MI 48109, USA; (L.H.); (H.-P.C.); (R.H.C.); (E.M.C.); (M.S.D.); (P.R.S.); (I.R.F.); (K.S.); (N.M.); (D.B.); (S.W.); (R.K.S.); (C.Z.)
| | - Martha Matuszak
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI 48109, USA;
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Ye L, Chen Y, Xu H, Wang Z, Li H, Qi J, Wang J, Yao J, Liu J, Song B. Radiomics of Contrast-Enhanced Computed Tomography: A Potential Biomarker for Pretreatment Prediction of the Response to Bacillus Calmette-Guerin Immunotherapy in Non-Muscle-Invasive Bladder Cancer. Front Cell Dev Biol 2022; 10:814388. [PMID: 35281100 PMCID: PMC8914064 DOI: 10.3389/fcell.2022.814388] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2021] [Accepted: 01/10/2022] [Indexed: 11/13/2022] Open
Abstract
Background:Bacillus Calmette-Guerin (BCG) instillation is recommended postoperatively after transurethral resection of bladder cancer (TURBT) in patients with high-risk non-muscle-invasive bladder cancer (NMIBC). An accurate prediction model for the BCG response can help identify patients with NMIBC who may benefit from alternative therapy.Objective: To investigate the value of computed tomography (CT) radiomics features in predicting the response to BCG instillation among patients with primary high-risk NMIBC.Methods: Patients with pathologically confirmed high-risk NMIBC were retrospectively reviewed. Patients who underwent contrast-enhanced CT examination within one to 2 weeks before TURBT and received ≥5 BCG instillation treatments in two independent hospitals were enrolled. Patients with a routine follow-up of at least 1 year at the outpatient department were included in the final cohort. Radiomics features based on CT images were extracted from the tumor and its periphery in the training cohort, and a radiomics signature was built with recursive feature elimination. Selected features further underwent an unsupervised radiomics analysis using the newly introduced method, non-negative matrix factorization (NMF), to compute factor factorization decompositions of the radiomics matrix. Finally, a robust component, which was most associated with BCG failure in 1 year, was selected. The performance of the selected component was assessed and tested in an external validation cohort.Results: Overall, 128 patients (training cohort, n = 104; external validation cohort, n = 24) were included, including 12 BCG failures in the training cohort and 11 failures in the validation cohort each. NMF revealed five components, of which component 3 was selected for the best discrimination of BCG failure; it had an area under the curve (AUC) of .79, sensitivity of .79, and specificity of .65 in the training set. In the external validation cohort, it achieved an AUC of .68, sensitivity of .73, and specificity of .69. Survival analysis showed that patients with higher component scores had poor recurrence-free survival (RFS) in both cohorts (C-index: training cohort, .69; validation cohort, .68).Conclusion: The study suggested that radiomics components based on NMF might be a potential biomarker to predict BCG response and RFS after BCG treatment in patients with high-risk NMIBC.
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Affiliation(s)
- Lei Ye
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Yuntian Chen
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Hui Xu
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Zhaoxiang Wang
- Department of Urology, Institute of Urology, West China Hospital, Sichuan University, Chengdu, China
| | | | - Jin Qi
- University of Electronic Science and Technology of China, Chengdu, China
| | - Jing Wang
- University of Electronic Science and Technology of China, Chengdu, China
| | - Jin Yao
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
- *Correspondence: Jin Yao, ; Jiaming Liu,
| | - Jiaming Liu
- Department of Urology, Institute of Urology, West China Hospital, Sichuan University, Chengdu, China
- *Correspondence: Jin Yao, ; Jiaming Liu,
| | - Bin Song
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
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Borhani S, Borhani R, Kajdacsy-balla A. Artificial intelligence: A promising frontier in bladder cancer diagnosis and outcome prediction. Crit Rev Oncol Hematol 2022. [DOI: 10.1016/j.critrevonc.2022.103601] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2021] [Revised: 01/12/2022] [Accepted: 01/17/2022] [Indexed: 02/07/2023] Open
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Gao RZ, Wen R, Wen DY, Huang J, Qin H, Li X, Wang XR, He Y, Yang H. Radiomics Analysis Based on Ultrasound Images to Distinguish the Tumor Stage and Pathological Grade of Bladder Cancer. J Ultrasound Med 2021; 40:2685-2697. [PMID: 33615528 DOI: 10.1002/jum.15659] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [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: 10/26/2020] [Revised: 01/21/2021] [Accepted: 01/31/2021] [Indexed: 05/28/2023]
Abstract
OBJECTIVES To identify the clinical value of ultrasound radiomic features in the preoperative prediction of tumor stage and pathological grade of bladder cancer (BLCA) patients. METHODS We retrospectively collected patients who had been diagnosed with BLCA by pathology. Ultrasound-based radiomic features were extracted from manually segmented regions of interest. Participants were randomly assigned to a training cohort and a validation cohort at a ratio of 7:3. Radiomic features were Z-score normalized and submitted to dimensional reduction analysis (including Spearman's correlation coefficient analysis, the random forest algorithm, and statistical testing) for core feature selection. Classifiers for tumor stage and pathological grade prediction were then constructed. Prediction performance was estimated by the area under the curve (AUC) of the receiver operating characteristic curve and was verified by the validation cohort. RESULTS A total of 5936 radiomic features were extracted from each of the ultrasound images obtained from 157 patients. The BLCA tumor stage and pathological grade prediction models were developed based on 30 and 35 features, respectively. Both models showed good predictive ability. For the tumor stage prediction model, the AUC was 0.94 in the training cohort and 0.84 in the validation cohort. For the pathological grade model, the AUCs obtained were 0.84 in the training cohort and 0.75 in the validation cohort. CONCLUSIONS The ultrasound-based radiomics models performed well in the preoperative tumor staging and pathological grading of BLCA. These findings should be applied clinically to optimize treatment and to assess prognoses for BLCA.
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Affiliation(s)
- Rui-Zhi Gao
- Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Rong Wen
- Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Dong-Yue Wen
- Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Jing Huang
- Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Hui Qin
- Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Xin Li
- GE Healthcare, Shanghai, China
| | | | - Yun He
- Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Hong Yang
- Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
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Galgano SJ, Porter KK, Burgan C, Rais-Bahrami S. The Role of Imaging in Bladder Cancer Diagnosis and Staging. Diagnostics (Basel) 2020; 10:E703. [PMID: 32948089 DOI: 10.3390/diagnostics10090703] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2020] [Revised: 09/02/2020] [Accepted: 09/10/2020] [Indexed: 01/23/2023] Open
Abstract
Bladder cancer (BC) is the most common cancer of the urinary tract in the United States. Imaging plays a significant role in the management of patients with BC, including the locoregional staging and evaluation for distant metastatic disease, which cannot be assessed at the time of cystoscopy and biopsy/resection. We aim to review the current role of cross-sectional and molecular imaging modalities for the staging and restaging of BC and the potential advantages and limitations of each imaging modality. CT is the most widely available and frequently utilized imaging modality for BC and demonstrates good performance for the detection of nodal and visceral metastatic disease. MRI offers potential value for the locoregional staging and evaluation of muscular invasion of BC, which is critically important for prognostication and treatment decision-making. FDG-PET/MRI is a novel hybrid imaging modality combining the advantages of both MRI and FDG-PET/CT in a single-setting comprehensive staging examination and may represent the future of BC imaging evaluation.
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Abstract
The National Cancer Institute's Quantitative Imaging Network (QIN) has thrived over the past 12 years with an emphasis on the development of image-based decision support software tools for improving measurements of imaging metrics. An overarching goal has been to develop advanced tools that could be translated into clinical trials to provide for improved prediction of response to therapeutic interventions. This article provides an overview of the successes in development and translation of new algorithms into the clinical workflow by the many research teams of the Quantitative Imaging Network.
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