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He C, Xu H, Yuan E, Ye L, Chen Y, Yao J, Song B. The accuracy and quality of image-based artificial intelligence for muscle-invasive bladder cancer prediction. Insights Imaging 2024; 15:185. [PMID: 39090234 PMCID: PMC11294512 DOI: 10.1186/s13244-024-01780-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2024] [Accepted: 07/10/2024] [Indexed: 08/04/2024] Open
Abstract
PURPOSE To evaluate the diagnostic performance of image-based artificial intelligence (AI) studies in predicting muscle-invasive bladder cancer (MIBC). (2) To assess the reporting quality and methodological quality of these studies by Checklist for Artificial Intelligence in Medical Imaging (CLAIM), Radiomics Quality Score (RQS), and Prediction model Risk of Bias Assessment Tool (PROBAST). MATERIALS AND METHODS We searched Medline, Embase, Web of Science, and The Cochrane Library databases up to October 30, 2023. The eligible studies were evaluated using CLAIM, RQS, and PROBAST. Pooled sensitivity, specificity, and the diagnostic performances of these models for MIBC were also calculated. RESULTS Twenty-one studies containing 4256 patients were included, of which 17 studies were employed for the quantitative statistical analysis. The CLAIM study adherence rate ranged from 52.5% to 75%, with a median of 64.1%. The RQS points of each study ranged from 2.78% to 50% points, with a median of 30.56% points. All models were rated as high overall ROB. The pooled area under the curve was 0.85 (95% confidence interval (CI) 0.81-0.88) for computed tomography, 0.92 (95% CI 0.89-0.94) for MRI, 0.89 (95% CI 0.86-0.92) for radiomics and 0.91 (95% CI 0.88-0.93) for deep learning, respectively. CONCLUSION Although AI-powered muscle-invasive bladder cancer-predictive models showed promising performance in the meta-analysis, the reporting quality and the methodological quality were generally low, with a high risk of bias. CRITICAL RELEVANCE STATEMENT Artificial intelligence might improve the management of patients with bladder cancer. Multiple models for muscle-invasive bladder cancer prediction were developed. Quality assessment is needed to promote clinical application. KEY POINTS Image-based artificial intelligence models could aid in the identification of muscle-invasive bladder cancer. Current studies had low reporting quality, low methodological quality, and a high risk of bias. Future studies could focus on larger sample sizes and more transparent reporting of pathological evaluation, model explanation, and failure and sensitivity analyses.
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Affiliation(s)
- Chunlei He
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, 610041, China
- Department of Radiology, Sanya People's Hospital, Sanya, Hainan, 572000, China
| | - Hui Xu
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Enyu Yuan
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Lei Ye
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Yuntian Chen
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Jin Yao
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Bin Song
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, 610041, China.
- Department of Radiology, Sanya People's Hospital, Sanya, Hainan, 572000, China.
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Hu M, Wei W, Zhang J, Wang S, Tong X, Fan Y, Cheng Q, Liu Y, Li J, Liu L. Impact of virtual monochromatic images of different low-energy levels in dual-energy CT on radiomics models for predicting muscle invasion in bladder cancer. Abdom Radiol (NY) 2024:10.1007/s00261-024-04459-6. [PMID: 38937340 DOI: 10.1007/s00261-024-04459-6] [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: 04/15/2024] [Revised: 06/11/2024] [Accepted: 06/15/2024] [Indexed: 06/29/2024]
Abstract
OBJECTIVE The purpose of this study was to investigate the impact of different low-energy virtual monochromatic images (VMIs) in dual-energy CT on the performance of radiomics models for predicting muscle invasive status in bladder cancer (BCa). MATERIALS AND METHODS A total of 127 patients with pathologically proven muscle-invasive BCa (n = 49) and non-muscle-invasive BCa (n = 78) were randomly allocated into the training and test cohorts at a ratio of 7:3. Feature extraction was performed on the venous phase images reconstructed at 40, 50, 60 and 70-keV (single-energy analysis) or in combination (multi-energy analysis). Recursive feature elimination (RFE) and the least absolute shrinkage and selection operator (LASSO) were employed to select the most relevant features associated with BCa. Models were built using a support vector machine (SVM) classifier. Diagnostic performance was assessed through receiver operating characteristic curves, evaluating sensitivity, specificity, accuracy, precision, and the area-under-the curve (AUC) values. RESULTS In the test cohort, the multi-energy model achieved the best diagnostic performance with AUC, sensitivity, specificity, accuracy, and precision of 0.917, 0.800, 0.833, 0.821, and 0.750, respectively. Conversely, the single-energy model exhibited lower AUC and sensitivity in predicting the muscle invasion status. CONCLUSIONS By combining information from VMIs of various energies, the multi-energy model displays superior performance in preoperatively predicting the muscle invasion status of bladder cancer.
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Affiliation(s)
- Mengting Hu
- Department of Radiology, First Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Wei Wei
- Department of Radiology, First Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Jingyi Zhang
- Department of Radiology, First Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Shigeng Wang
- Department of Radiology, First Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Xiaoyu Tong
- Department of Radiology, First Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Yong Fan
- Department of Radiology, First Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Qiye Cheng
- Department of Radiology, First Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Yijun Liu
- Department of Radiology, First Affiliated Hospital of Dalian Medical University, Dalian, China
| | | | - Lei Liu
- Department of Urology, First Affiliated Hospital of Dalian Medical University, Xigang District, Lianhe Road, No.193, Dalian, China.
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Chen X, Zhuang Z, Pen L, Xue J, Zhu H, Zhang L, Wang D. Intratumoral and peritumoral CT-based radiomics for predicting the microsatellite instability in gastric cancer. Abdom Radiol (NY) 2024; 49:1363-1375. [PMID: 38305796 DOI: 10.1007/s00261-023-04165-9] [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: 10/17/2023] [Revised: 12/13/2023] [Accepted: 12/14/2023] [Indexed: 02/03/2024]
Abstract
PURPOSE To investigate the value of intratumoral and peritumoral radiomics based on contrast-enhanced computer tomography (CECT) to preoperatively predict microsatellite instability (MSI) status in gastric cancer (GC) patients. METHODS A total of 189 GC patients, including 63 patients with MSI-high (MSI-H) and 126 patients with MSI-low/stable (MSI-L/S), were randomly divided into the training cohort and validation cohort. Intratumoral and 5-mm peritumoral regions' radiomics features were extracted from CECT images. The features were standardized by Z-score, and the Inter- and intraclass correlation coefficient, univariate logistic regression analysis, and least absolute shrinkage and selection operator (LASSO) were applied to select the optimal radiomics features. Radiomics scores (Rad-score) based on intratumoral regions, peritumoral regions, and intratumoral + 5-mm peritumoral regions were calculated by weighting the linear combination of the selected features with their respective coefficients to construct the intratumoral model, peritumoral model, and intratumoral + peritumoral model. Logistic regression was used to establish a combined model by combining clinical characteristics, CT semantic features, and Rad-score of intratumoral and peritumoral regions. RESULTS Eleven radiomics features were selected to establish a radiomics intratumoral + peritumoral model. CT-measured tumor length and tumor location were independent risk factors for MSI status. The established combined model obtained the highest area under the receiver operating characteristic (ROC) curve (AUC) of 0.830 (95% CI, 0.727-0.906) in the validation cohort. The calibration curve and decision curve demonstrated its good model fitness and clinical application value. CONCLUSION The combined model based on intratumoral and peritumoral CECT radiomics features and clinical factors can predict the MSI status of GS with moderate accuracy before surgery, which helps formulate personalized treatment strategies.
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Affiliation(s)
- Xingchi Chen
- Department of Medical Imaging, The Affiliated Hospital of Jiangsu University, Zhenjiang, 212001, Jiangsu Province, China
| | - Zijian Zhuang
- Department of Medical Imaging, The Affiliated Hospital of Jiangsu University, Zhenjiang, 212001, Jiangsu Province, China
| | - Lin Pen
- School of Medicine, Jiangsu University, Zhenjiang, 212001, Jiangsu Province, China
| | - Jing Xue
- School of Medicine, Jiangsu University, Zhenjiang, 212001, Jiangsu Province, China
| | - Haitao Zhu
- Department of Medical Imaging, The Affiliated Hospital of Jiangsu University, Zhenjiang, 212001, Jiangsu Province, China
| | - Lirong Zhang
- Department of Medical Imaging, The Affiliated Hospital of Jiangsu University, Zhenjiang, 212001, Jiangsu Province, China.
- Institute of Imaging and Artificial Intelligence, Jiangsu University, Zhenjiang, 212000, Jiangsu Province, China.
| | - Dongqing Wang
- Department of Medical Imaging, The Affiliated Hospital of Jiangsu University, Zhenjiang, 212001, Jiangsu Province, China.
- Institute of Imaging and Artificial Intelligence, Jiangsu University, Zhenjiang, 212000, Jiangsu Province, China.
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Wei Z, Xv Y, Liu H, Li Y, Yin S, Xie Y, Chen Y, Lv F, Jiang Q, Li F, Xiao M. A CT-based deep learning model predicts overall survival in patients with muscle invasive bladder cancer after radical cystectomy: a multicenter retrospective cohort study. Int J Surg 2024; 110:2922-2932. [PMID: 38349205 PMCID: PMC11093481 DOI: 10.1097/js9.0000000000001194] [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: 11/28/2023] [Accepted: 01/31/2024] [Indexed: 05/16/2024]
Abstract
BACKGROUND Muscle invasive bladder cancer (MIBC) has a poor prognosis even after radical cystectomy (RC). Postoperative survival stratification based on radiomics and deep learning (DL) algorithms may be useful for treatment decision-making and follow-up management. This study was aimed to develop and validate a DL model based on preoperative computed tomography (CT) for predicting postcystectomy overall survival (OS) in patients with MIBC. METHODS MIBC patients who underwent RC were retrospectively included from four centers, and divided into the training, internal validation, and external validation sets. A DL model incorporated the convolutional block attention module (CBAM) was built for predicting OS using preoperative CT images. The authors assessed the prognostic accuracy of the DL model and compared it with classic handcrafted radiomics model and clinical model. Then, a deep learning radiomics nomogram (DLRN) was developed by combining clinicopathological factors, radiomics score (Rad-score) and deep learning score (DL-score). Model performance was assessed by C-index, KM curve, and time-dependent ROC curve. RESULTS A total of 405 patients with MIBC were included in this study. The DL-score achieved a much higher C-index than Rad-score and clinical model (0.690 vs. 0.652 vs. 0.618 in the internal validation set, and 0.658 vs. 0.601 vs. 0.610 in the external validation set). After adjusting for clinicopathologic variables, the DL-score was identified as a significantly independent risk factor for OS by the multivariate Cox regression analysis in all sets (all P <0.01). The DLRN further improved the performance, with a C-index of 0.713 (95% CI: 0.627-0.798) in the internal validation set and 0.685 (95% CI: 0.586-0.765) in external validation set, respectively. CONCLUSIONS A DL model based on preoperative CT can predict survival outcome of patients with MIBC, which may help in risk stratification and guide treatment decision-making and follow-up management.
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Affiliation(s)
| | | | | | | | - Siwen Yin
- Department of Urology, Chongqing University Fuling Hospital
| | | | - Yong Chen
- Department of Urology, Chongqing University Fuling Hospital
| | - Fajin Lv
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University
| | - Qing Jiang
- Department of Urology, The Second Affiliated Hospital of Chongqing Medical University
| | - Feng Li
- Department of Urology, Chongqing University Three Gorges Hospital, Chongqing, People’s Republic of China
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Zhou Y, Zheng X, Sun Z, Wang B. Analysis of Bladder Cancer Staging Prediction Using Deep Residual Neural Network, Radiomics, and RNA-Seq from High-Definition CT Images. Genet Res (Camb) 2024; 2024:4285171. [PMID: 38715622 PMCID: PMC11074870 DOI: 10.1155/2024/4285171] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Revised: 03/04/2024] [Accepted: 03/19/2024] [Indexed: 06/30/2024] Open
Abstract
Bladder cancer has recently seen an alarming increase in global diagnoses, ascending as a predominant cause of cancer-related mortalities. Given this pressing scenario, there is a burgeoning need to identify effective biomarkers for both the diagnosis and therapeutic guidance of bladder cancer. This study focuses on evaluating the potential of high-definition computed tomography (CT) imagery coupled with RNA-sequencing analysis to accurately predict bladder tumor stages, utilizing deep residual networks. Data for this study, including CT images and RNA-Seq datasets for 82 high-grade bladder cancer patients, were sourced from the TCIA and TCGA databases. We employed Cox and lasso regression analyses to determine radiomics and gene signatures, leading to the identification of a three-factor radiomics signature and a four-gene signature in our bladder cancer cohort. ROC curve analyses underscored the strong predictive capacities of both these signatures. Furthermore, we formulated a nomogram integrating clinical features, radiomics, and gene signatures. This nomogram's AUC scores stood at 0.870, 0.873, and 0.971 for 1-year, 3-year, and 5-year predictions, respectively. Our model, leveraging radiomics and gene signatures, presents significant promise for enhancing diagnostic precision in bladder cancer prognosis, advocating for its clinical adoption.
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Affiliation(s)
- Yao Zhou
- Department of Radiology, Affiliated Hospital of Guizhou Medical University, Guiyang, China
| | - Xingju Zheng
- Department of Radiology, Guizhou Provincial People's Hospital, Guiyang, China
| | - Zhucheng Sun
- Interventional Radiology, Guizhou Provincial People's Hospital, Guiyang, China
| | - Bo Wang
- Department of Radiology, Affiliated Hospital of Guizhou Medical University, Guiyang, China
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Zhang R, Jia S, Zhai L, Wu F, Zhang S, Li F. Predicting preoperative muscle invasion status for bladder cancer using computed tomography-based radiomics nomogram. BMC Med Imaging 2024; 24:98. [PMID: 38678222 PMCID: PMC11055285 DOI: 10.1186/s12880-024-01276-7] [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: 11/11/2023] [Accepted: 04/19/2024] [Indexed: 04/29/2024] Open
Abstract
OBJECTIVES The aim of the study is to assess the efficacy of the established computed tomography (CT)-based radiomics nomogram combined with radiomics and clinical features for predicting muscle invasion status in bladder cancer (BCa). METHODS A retrospective analysis was conducted using data from patients who underwent CT urography at our institution between May 2018 and April 2023 with urothelial carcinoma of the bladder confirmed by postoperative histology. There were 196 patients enrolled in all, and each was randomized at random to either the training cohort (n = 137) or the test cohort (n = 59). Eight hundred fifty-one radiomics features in all were retrieved. For feature selection, the significance test and least absolute shrinkage and selection operator (LASSO) approaches were utilized. Subsequently, the radiomics score (Radscore) was obtained by applying linear weighting based on the selected features. The clinical and radiomics model, as well as radiomics-clinical nomogram were all established using logistic regression. Three models were evaluated using analysis of the receiver operating characteristic curve. An area under the curve (AUC) and 95% confidence intervals (CI) as well as specificity, sensitivity, accuracy, negative predictive value, and positive predictive value were included in the analysis. Radiomics-clinical nomogram's performance was assessed based on discrimination, calibration, and clinical utility. RESULTS After obtaining 851 radiomics features, 12 features were ultimately selected. Histopathological grading and tortuous blood vessels were included in the clinical model. The Radscore and clinical histopathology grading were among the final predictors in the unique nomogram. The three models had an AUC of 0.811 (95% CI, 0.742-0.880), 0.845 (95% CI, 0.781-0.908), and 0.896 (95% CI, 0.846-0.947) in the training cohort and in the test cohort they were 0.808 (95% CI, 0.703-0.913), 0.847 (95% CI, 0.739-0.954), and 0.887 (95% CI, 0.803-0.971). According to the DeLong test, the radiomics-clinical nomogram's AUC in the training cohort substantially differed from that of the clinical model (AUC: 0.896 versus 0.845, p = 0.015) and the radiomics model (AUC: 0.896 versus 0.811, p = 0.002). The Delong test in the test cohort revealed no significant difference among the three models. CONCLUSIONS CT-based radiomics-clinical nomogram can be a useful tool for quantitatively predicting the status of muscle invasion in BCa.
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Affiliation(s)
- Rui Zhang
- Department of Radiology, Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, Xiangyang, 441021, Hubei, China
| | - Shijun Jia
- Department of Radiology, Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, Xiangyang, 441021, Hubei, China
| | - Linhan Zhai
- Department of Radiology, Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, Xiangyang, 441021, Hubei, China
| | - Feng Wu
- Department of Radiology, Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, Xiangyang, 441021, Hubei, China
| | - Shuang Zhang
- Department of Radiology, Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, Xiangyang, 441021, Hubei, China.
| | - Feng Li
- Department of Radiology, Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, Xiangyang, 441021, Hubei, China.
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Feretzakis G, Juliebø-Jones P, Tsaturyan A, Sener TE, Verykios VS, Karapiperis D, Bellos T, Katsimperis S, Angelopoulos P, Varkarakis I, Skolarikos A, Somani B, Tzelves L. Emerging Trends in AI and Radiomics for Bladder, Kidney, and Prostate Cancer: A Critical Review. Cancers (Basel) 2024; 16:810. [PMID: 38398201 PMCID: PMC10886599 DOI: 10.3390/cancers16040810] [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: 01/06/2024] [Revised: 02/02/2024] [Accepted: 02/14/2024] [Indexed: 02/25/2024] Open
Abstract
This comprehensive review critically examines the transformative impact of artificial intelligence (AI) and radiomics in the diagnosis, prognosis, and management of bladder, kidney, and prostate cancers. These cutting-edge technologies are revolutionizing the landscape of cancer care, enhancing both precision and personalization in medical treatments. Our review provides an in-depth analysis of the latest advancements in AI and radiomics, with a specific focus on their roles in urological oncology. We discuss how AI and radiomics have notably improved the accuracy of diagnosis and staging in bladder cancer, especially through advanced imaging techniques like multiparametric MRI (mpMRI) and CT scans. These tools are pivotal in assessing muscle invasiveness and pathological grades, critical elements in formulating treatment plans. In the realm of kidney cancer, AI and radiomics aid in distinguishing between renal cell carcinoma (RCC) subtypes and grades. The integration of radiogenomics offers a comprehensive view of disease biology, leading to tailored therapeutic approaches. Prostate cancer diagnosis and management have also seen substantial benefits from these technologies. AI-enhanced MRI has significantly improved tumor detection and localization, thereby aiding in more effective treatment planning. The review also addresses the challenges in integrating AI and radiomics into clinical practice, such as the need for standardization, ensuring data quality, and overcoming the "black box" nature of AI. We emphasize the importance of multicentric collaborations and extensive studies to enhance the applicability and generalizability of these technologies in diverse clinical settings. In conclusion, AI and radiomics represent a major paradigm shift in oncology, offering more precise, personalized, and patient-centric approaches to cancer care. While their potential to improve diagnostic accuracy, patient outcomes, and our understanding of cancer biology is profound, challenges in clinical integration and application persist. We advocate for continued research and development in AI and radiomics, underscoring the need to address existing limitations to fully leverage their capabilities in the field of oncology.
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Affiliation(s)
- Georgios Feretzakis
- School of Science and Technology, Hellenic Open University, 26335 Patras, Greece; (G.F.); (V.S.V.)
| | - Patrick Juliebø-Jones
- Department of Urology, Haukeland University Hospital, 5021 Bergen, Norway;
- Department of Clinical, Medicine University of Bergen, 5021 Bergen, Norway
- European Association of Urology, Young Academic Urologists, Urolithiasis Group, NL-6803 Arnhem, The Netherlands; (A.T.); (T.E.S.)
| | - Arman Tsaturyan
- European Association of Urology, Young Academic Urologists, Urolithiasis Group, NL-6803 Arnhem, The Netherlands; (A.T.); (T.E.S.)
- Department of Urology, Erebouni Medical Center, Yerevan 0087, Armenia
| | - Tarik Emre Sener
- European Association of Urology, Young Academic Urologists, Urolithiasis Group, NL-6803 Arnhem, The Netherlands; (A.T.); (T.E.S.)
- Department of Urology, Marmara University School of Medicine, Istanbul 34854, Turkey
| | - Vassilios S. Verykios
- School of Science and Technology, Hellenic Open University, 26335 Patras, Greece; (G.F.); (V.S.V.)
| | - Dimitrios Karapiperis
- School of Science and Technology, International Hellenic University, 57001 Thessaloniki, Greece;
| | - Themistoklis Bellos
- Second Department of Urology, Sismanoglio Hospital, National and Kapodistrian University of Athens, 15126 Athens, Greece; (T.B.); (S.K.); (P.A.); (I.V.); (A.S.)
| | - Stamatios Katsimperis
- Second Department of Urology, Sismanoglio Hospital, National and Kapodistrian University of Athens, 15126 Athens, Greece; (T.B.); (S.K.); (P.A.); (I.V.); (A.S.)
| | - Panagiotis Angelopoulos
- Second Department of Urology, Sismanoglio Hospital, National and Kapodistrian University of Athens, 15126 Athens, Greece; (T.B.); (S.K.); (P.A.); (I.V.); (A.S.)
| | - Ioannis Varkarakis
- Second Department of Urology, Sismanoglio Hospital, National and Kapodistrian University of Athens, 15126 Athens, Greece; (T.B.); (S.K.); (P.A.); (I.V.); (A.S.)
| | - Andreas Skolarikos
- Second Department of Urology, Sismanoglio Hospital, National and Kapodistrian University of Athens, 15126 Athens, Greece; (T.B.); (S.K.); (P.A.); (I.V.); (A.S.)
| | - Bhaskar Somani
- Department of Urology, University of Southampton, Southampton SO17 1BJ, UK;
| | - Lazaros Tzelves
- European Association of Urology, Young Academic Urologists, Urolithiasis Group, NL-6803 Arnhem, The Netherlands; (A.T.); (T.E.S.)
- Second Department of Urology, Sismanoglio Hospital, National and Kapodistrian University of Athens, 15126 Athens, Greece; (T.B.); (S.K.); (P.A.); (I.V.); (A.S.)
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Wei Z, Liu H, Xv Y, Liao F, He Q, Xie Y, Lv F, Jiang Q, Xiao M. Development and validation of a CT-based deep learning radiomics nomogram to predict muscle invasion in bladder cancer. Heliyon 2024; 10:e24878. [PMID: 38304824 PMCID: PMC10831750 DOI: 10.1016/j.heliyon.2024.e24878] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2023] [Revised: 01/16/2024] [Accepted: 01/16/2024] [Indexed: 02/03/2024] Open
Abstract
Objective This study aimed to develop a nomogram combining CT-based handcrafted radiomics and deep learning (DL) features to preoperatively predict muscle invasion in bladder cancer (BCa) with multi-center validation. Methods In this retrospective study, 323 patients underwent radical cystectomy with pathologically confirmed BCa were enrolled and randomly divided into the training cohort (n = 226) and internal validation cohort (n = 97). And fifty-two patients from another independent medical center were enrolled as an independent external validation cohort. Handcrafted radiomics and DL features were constructed from preoperative nephrographic phase CT images. Least absolute shrinkage and selection operator (LASSO) regression was used to identify the most discriminative features in train cohort. Multivariate logistic regression was used to develop the predictive model and a deep learning radiomics nomogram (DLRN) was constructed. The predictive performance of models was evaluated by area under the curves (AUC) in the three cohorts. The calibration and clinical usefulness of DLRN were estimated by calibration curve and decision curve analysis. Results The nomogram that incorporated radiomics signature and DL signature demonstrated satisfactory predictive performance for differentiating non-muscle invasive bladder cancer (NMIBC) from muscle invasive bladder cancer (MIBC), with an AUC of 0.884 (95 % CI: 0.813-0.953) in internal validation cohort and 0.862 (95 % CI: 0.756-0.968) in external validation cohort, respectively. Decision curve analysis confirmed the clinical usefulness of the nomogram. Conclusions A CT-based deep learning radiomics nomogram exhibited a promising performance for preoperative prediction of muscle invasion in bladder cancer, and may be helpful in the clinical decision-making process.
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Affiliation(s)
- Zongjie Wei
- Department of Urology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Huayun Liu
- Department of Urology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Yingjie Xv
- Department of Urology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Fangtong Liao
- Department of Urology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Quanhao He
- Department of Urology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Yongpeng Xie
- Department of Urology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Fajin Lv
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Qing Jiang
- Department of Urology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Mingzhao Xiao
- Department of Urology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
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Yang H, Liu H, Lin J, Xiao H, Guo Y, Mei H, Ding Q, Yuan Y, Lai X, Wu K, Wu S. An automatic texture feature analysis framework of renal tumor: surgical, pathological, and molecular evaluation based on multi-phase abdominal CT. Eur Radiol 2024; 34:355-366. [PMID: 37528301 DOI: 10.1007/s00330-023-10016-4] [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: 04/19/2023] [Revised: 06/06/2023] [Accepted: 06/12/2023] [Indexed: 08/03/2023]
Abstract
OBJECTIVES To determine whether the texture feature analysis of multi-phase abdominal CT can provide a robust prediction of benign and malignant, histological subtype, pathological stage, nephrectomy risk, pathological grade, and Ki67 index in renal tumor. METHODS A total of 1051 participants with renal tumor were split into the internal cohort (850 patients from four different hospitals) and the external testing cohort (201 patients from another local hospital). The proposed framework comprised a 3D-kidney and tumor segmentation model by 3D-UNet, a feature extractor for the regions of interest based on radiomics and image dimension reduction, and the six classifiers by XGBoost. A quantitative model interpretation method called SHAP was used to explore the contribution of each feature. RESULTS The proposed multi-phase abdominal CT model provides robust prediction for benign and malignant, histological subtype, pathological stage, nephrectomy risk, pathological grade, and Ki67 index in the internal validation set, with the AUROC values of 0.88 ± 0.1, 0.90 ± 0.1, 0.91 ± 0.1, 0.89 ± 0.1, 0.84 ± 0.1, and 0.88 ± 0.1, respectively. The external testing set also showed impressive results, with AUROC values of 0.83 ± 0.1, 0.83 ± 0.1, 0.85 ± 0.1, 0.81 ± 0.1, 0.79 ± 0.1, and 0.81 ± 0.1, respectively. The radiomics feature including the first-order statistics, the tumor size-related morphology, and the shape-related tumor features contributed most to the model predictions. CONCLUSIONS Automatic texture feature analysis of abdominal multi-phase CT provides reliable predictions for multi-tasks, suggesting the potential usage of clinical application. CLINICAL RELEVANCE STATEMENT The automatic texture feature analysis framework, based on multi-phase abdominal CT, provides robust and reliable predictions for multi-tasks. These valuable insights can serve as a guiding tool for clinical diagnosis and treatment, making medical imaging an essential component in the process. KEY POINTS • The automatic texture feature analysis framework based on multi-phase abdominal CT can provide more accurate prediction of benign and malignant, histological subtype, pathological stage, nephrectomy risk, pathological grade, and Ki67 index in renal tumor. • The quantitative decomposition of the prediction model was conducted to explore the contribution of the extracted feature. • The study involving 1051 patients from 5 medical centers, along with a heterogeneous external data testing strategy, can be seamlessly transferred to various tasks involving new datasets.
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Affiliation(s)
- Huancheng Yang
- Luohu Clinical Institute, Shantou University Medical College, Shantou, 515000, China
- Department of Urology, The Third Affiliated Hospital of Shenzhen University (Luohu Hospital Group), Shenzhen, 518000, China
- Shenzhen Following Precision Medical Research Institute, The Third Affiliated Hospital of Shenzhen University (Luohu Hospital Group), Shenzhen, 518000, China
- Shantou University Medical College, Shantou University, Shantou, 515000, China
| | - Hanlin Liu
- Department of Radiology, The Third Affiliated Hospital of Shenzhen University (Luohu Hospital Group), Shenzhen, 518000, China
| | - Jiashan Lin
- Luohu Clinical Institute, Shantou University Medical College, Shantou, 515000, China
- Shantou University Medical College, Shantou University, Shantou, 515000, China
- Department of Urology, Peking University Shenzhen Hospital, Shenzhen, 518036, China
| | - Hongwei Xiao
- Department of Urology, The Third Affiliated Hospital of Shenzhen University (Luohu Hospital Group), Shenzhen, 518000, China
- Shenzhen Following Precision Medical Research Institute, The Third Affiliated Hospital of Shenzhen University (Luohu Hospital Group), Shenzhen, 518000, China
| | - Yiqi Guo
- Luohu Clinical Institute, Shantou University Medical College, Shantou, 515000, China
- Shantou University Medical College, Shantou University, Shantou, 515000, China
| | - Hangru Mei
- Department of Urology, The Third Affiliated Hospital of Shenzhen University (Luohu Hospital Group), Shenzhen, 518000, China
- Shenzhen Following Precision Medical Research Institute, The Third Affiliated Hospital of Shenzhen University (Luohu Hospital Group), Shenzhen, 518000, China
| | - Qiuxia Ding
- Department of Urology, The Third Affiliated Hospital of Shenzhen University (Luohu Hospital Group), Shenzhen, 518000, China
- Shenzhen Following Precision Medical Research Institute, The Third Affiliated Hospital of Shenzhen University (Luohu Hospital Group), Shenzhen, 518000, China
| | - Yangguang Yuan
- Department of Radiology, The Third Affiliated Hospital of Shenzhen University (Luohu Hospital Group), Shenzhen, 518000, China
| | - Xiaohui Lai
- Department of Radiology, The Third Affiliated Hospital of Shenzhen University (Luohu Hospital Group), Shenzhen, 518000, China
| | - Kai Wu
- Department of Urology, The Third Affiliated Hospital of Shenzhen University (Luohu Hospital Group), Shenzhen, 518000, China.
- Shenzhen Following Precision Medical Research Institute, The Third Affiliated Hospital of Shenzhen University (Luohu Hospital Group), Shenzhen, 518000, China.
| | - Song Wu
- Luohu Clinical Institute, Shantou University Medical College, Shantou, 515000, China.
- Shenzhen Following Precision Medical Research Institute, The Third Affiliated Hospital of Shenzhen University (Luohu Hospital Group), Shenzhen, 518000, China.
- Shantou University Medical College, Shantou University, Shantou, 515000, China.
- Department of Urology, Health Science Center, South China Hospital, Shenzhen University, Shenzhen, 518116, China.
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Gelikman DG, Rais-Bahrami S, Pinto PA, Turkbey B. AI-powered radiomics: revolutionizing detection of urologic malignancies. Curr Opin Urol 2024; 34:1-7. [PMID: 37909882 PMCID: PMC10842165 DOI: 10.1097/mou.0000000000001144] [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] [Indexed: 11/03/2023]
Abstract
PURPOSE OF REVIEW This review aims to highlight the integration of artificial intelligence-powered radiomics in urologic oncology, focusing on the diagnostic and prognostic advancements in the realm of managing prostate, kidney, and bladder cancers. RECENT FINDINGS As artificial intelligence continues to shape the medical imaging landscape, its integration into the field of urologic oncology has led to impressive results. For prostate cancer diagnostics, machine learning has shown promise in refining clinically-significant lesion detection, with some success in deciphering ambiguous lesions on multiparametric MRI. For kidney cancer, radiomics has emerged as a valuable tool for better distinguishing between benign and malignant renal masses and predicting tumor behavior from CT or MRI scans. Meanwhile, in the arena of bladder cancer, there is a burgeoning emphasis on prediction of muscle invasive cancer and forecasting disease trajectory. However, many studies showing promise in these areas face challenges due to limited sample sizes and the need for broader external validation. SUMMARY Radiomics integrated with artificial intelligence offers a pioneering approach to urologic oncology, ushering in an era of enhanced diagnostic precision and reduced invasiveness, guiding patient-tailored treatment plans. Researchers must embrace broader, multicentered endeavors to harness the full potential of this field.
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Affiliation(s)
- David G Gelikman
- Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Soroush Rais-Bahrami
- Department of Urology, The University of Alabama at Birmingham Heersink School of Medicine, Birmingham, AL, USA
- O’Neal Comprehensive Cancer Center, The University of Alabama at Birmingham Heersink School of Medicine, Birmingham, AL, USA
- Department of Radiology, The University of Alabama at Birmingham Heersink School of Medicine, Birmingham, AL, USA
| | - Peter A Pinto
- Urologic Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Baris Turkbey
- Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
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Yang H, Wu K, Liu H, Wu P, Yuan Y, Wang L, Liu Y, Zeng H, Li J, Liu W, Wu S. An automated surgical decision-making framework for partial or radical nephrectomy based on 3D-CT multi-level anatomical features in renal cell carcinoma. Eur Radiol 2023; 33:7532-7541. [PMID: 37289245 PMCID: PMC10598088 DOI: 10.1007/s00330-023-09812-9] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2023] [Revised: 03/14/2023] [Accepted: 03/27/2023] [Indexed: 06/09/2023]
Abstract
OBJECTIVES To determine whether 3D-CT multi-level anatomical features can provide a more accurate prediction of surgical decision-making for partial or radical nephrectomy in renal cell carcinoma. METHODS This is a retrospective study based on multi-center cohorts. A total of 473 participants with pathologically proved renal cell carcinoma were split into the internal training and the external testing set. The training set contains 412 cases from five open-source cohorts and two local hospitals. The external testing set includes 61 participants from another local hospital. The proposed automatic analytic framework contains the following modules: a 3D kidney and tumor segmentation model constructed by 3D-UNet, a multi-level feature extractor based on the region of interest, and a partial or radical nephrectomy prediction classifier by XGBoost. The fivefold cross-validation strategy was used to get a robust model. A quantitative model interpretation method called the Shapley Additive Explanations was conducted to explore the contribution of each feature. RESULTS In the prediction of partial versus radical nephrectomy, the combination of multi-level features achieved better performance than any single-level feature. For the internal validation, the AUROC was 0.93 ± 0.1, 0.94 ± 0.1, 0.93 ± 0.1, 0.93 ± 0.1, and 0.93 ± 0.1, respectively, as determined by the fivefold cross-validation. The AUROC from the optimal model was 0.82 ± 0.1 in the external testing set. The tumor shape Maximum 3D Diameter plays the most vital role in the model decision. CONCLUSIONS The automated surgical decision framework for partial or radical nephrectomy based on 3D-CT multi-level anatomical features exhibits robust performance in renal cell carcinoma. The framework points the way towards guiding surgery through medical images and machine learning. CLINICAL RELEVANCE STATEMENT We proposed an automated analytic framework that can assist surgeons in partial or radical nephrectomy decision-making. The framework points the way towards guiding surgery through medical images and machine learning. KEY POINTS • The 3D-CT multi-level anatomical features provide a more accurate prediction of surgical decision-making for partial or radical nephrectomy in renal cell carcinoma. • The data from multicenter study and a strict fivefold cross-validation strategy, both internal validation set and external testing set, can be easily transferred to different tasks of new datasets. • The quantitative decomposition of the prediction model was conducted to explore the contribution of each extracted feature.
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Affiliation(s)
- Huancheng Yang
- Teaching Center of Shenzhen Luohu Hospital, Shantou University Medical College, Shantou, 515000, China
- Department of Urology, The Third Affiliated Hospital of Shenzhen University (Luohu Hospital Group), Shenzhen, 518000, China
- Shantou University Medical College, Shantou University, Shantou, 515000, China
- Shenzhen Following Precision Medical Research Institute, Luohu Hospital Group, Shenzhen, 51800, China
| | - Kai Wu
- Department of Urology, The Third Affiliated Hospital of Shenzhen University (Luohu Hospital Group), Shenzhen, 518000, China
- Shenzhen Following Precision Medical Research Institute, Luohu Hospital Group, Shenzhen, 51800, China
| | - Hanlin Liu
- Department of Radiology, The Third Affiliated Hospital of Shenzhen University (Luohu Hospital Group), Shenzhen, 518000, China
| | - Peng Wu
- Department of Urology, The Third Affiliated Hospital of Shenzhen University (Luohu Hospital Group), Shenzhen, 518000, China
- Shenzhen Following Precision Medical Research Institute, Luohu Hospital Group, Shenzhen, 51800, China
| | - Yangguang Yuan
- Department of Radiology, The Third Affiliated Hospital of Shenzhen University (Luohu Hospital Group), Shenzhen, 518000, China
| | - Lei Wang
- Department of Radiology, The Third Affiliated Hospital of Shenzhen University (Luohu Hospital Group), Shenzhen, 518000, China
| | - Yaru Liu
- Department of Radiology, The Third Affiliated Hospital of Shenzhen University (Luohu Hospital Group), Shenzhen, 518000, China
| | - Haoyang Zeng
- Teaching Center of Shenzhen Luohu Hospital, Shantou University Medical College, Shantou, 515000, China
- Shantou University Medical College, Shantou University, Shantou, 515000, China
| | - Junkai Li
- Teaching Center of Shenzhen Luohu Hospital, Shantou University Medical College, Shantou, 515000, China
- Shantou University Medical College, Shantou University, Shantou, 515000, China
| | - Weihao Liu
- Teaching Center of Shenzhen Luohu Hospital, Shantou University Medical College, Shantou, 515000, China
- Shantou University Medical College, Shantou University, Shantou, 515000, China
| | - Song Wu
- Teaching Center of Shenzhen Luohu Hospital, Shantou University Medical College, Shantou, 515000, China.
- Shenzhen Following Precision Medical Research Institute, Luohu Hospital Group, Shenzhen, 51800, China.
- Department of Urology, South China Hospital, Health Science Center, Shenzhen University, Shenzhen, 518116, China.
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Xiong S, Dong W, Deng Z, Jiang M, Li S, Hu B, Liu X, Chen L, Xu S, Fan B, Fu B. Value of the application of computed tomography-based radiomics for preoperative prediction of unfavorable pathology in initial bladder cancer. Cancer Med 2023; 12:15868-15880. [PMID: 37434436 PMCID: PMC10469743 DOI: 10.1002/cam4.6225] [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: 02/20/2023] [Revised: 05/15/2023] [Accepted: 06/01/2023] [Indexed: 07/13/2023] Open
Abstract
OBJECTIVES To construct and validate unfavorable pathology (UFP) prediction models for patients with the first diagnosis of bladder cancer (initial BLCA) and to compare the comprehensive predictive performance of these models. MATERIALS AND METHODS A total of 105 patients with initial BLCA were included and randomly enrolled into the training and testing cohorts in a 7:3 ratio. The clinical model was constructed using independent UFP-risk factors determined by multivariate logistic regression (LR) analysis in the training cohort. Radiomics features were extracted from manually segmented regions of interest in computed tomography (CT) images. The optimal CT-based radiomics features to predict UFP were determined by the optimal feature filter and the least absolute shrinkage and selection operator algorithm. The radiomics model consist with the optimal features was constructed by the best of the six machine learning filters. The clinic-radiomics model combined the clinical and radiomics models via LR. The area under the curve (AUC), accuracy, sensitivity, specificity, positive and negative predictive value, calibration curve and decision curve analysis were used to evaluate the predictive performance of the models. RESULTS Patients in the UFP group had a significantly older age (69.61 vs. 63.93 years, p = 0.034), lager tumor size (45.7% vs. 11.1%, p = 0.002) and higher neutrophil to lymphocyte ratio (NLR; 2.76 vs. 2.33, p = 0.017) than favorable pathologic group in the training cohort. Tumor size (OR, 6.02; 95% CI, 1.50-24.10; p = 0.011) and NLR (OR, 1.50; 95% CI, 1.05-2.16; p = 0.026) were identified as independent predictive factors for UFP, and the clinical model was constructed using these factors. The LR classifier with the best AUC (0.817, the testing cohorts) was used to construct the radiomics model based on the optimal radiomics features. Finally, the clinic-radiomics model was developed by combining the clinical and radiomics models using LR. After comparison, the clinic-radiomics model had the best performance in comprehensive predictive efficacy (accuracy = 0.750, AUC = 0.817, the testing cohorts) and clinical net benefit among UFP-prediction models, while the clinical model (accuracy = 0.625, AUC = 0.742, the testing cohorts) was the worst. CONCLUSION Our study demonstrates that the clinic-radiomics model exhibits the best predictive efficacy and clinical net benefit for predicting UFP in initial BLCA compared with the clinical and radiomics model. The integration of radiomics features significantly improves the comprehensive performance of the clinical model.
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Affiliation(s)
- Situ Xiong
- Department of UrologyThe First Affiliated Hospital of Nanchang UniversityNanchangChina
- Jiangxi Institute of UrologyNanchangChina
| | - Wentao Dong
- Department of RadiologyJiangxi Provincial People's Hospital, The First Affiliated Hospital of Nanchang Medical CollegeNanchangChina
| | - Zhikang Deng
- Department of Nuclear Medicine, Jiangxi Provincial People's HospitalThe First Affiliated Hospital of Nanchang Medical CollegeNanchangChina
| | - Ming Jiang
- Department of UrologyThe First Affiliated Hospital of Nanchang UniversityNanchangChina
- Jiangxi Institute of UrologyNanchangChina
| | - Sheng Li
- Department of UrologyThe First Affiliated Hospital of Nanchang UniversityNanchangChina
- Jiangxi Institute of UrologyNanchangChina
| | - Bing Hu
- Department of UrologyThe First Affiliated Hospital of Nanchang UniversityNanchangChina
- Jiangxi Institute of UrologyNanchangChina
| | - Xiaoqiang Liu
- Department of UrologyThe First Affiliated Hospital of Nanchang UniversityNanchangChina
- Jiangxi Institute of UrologyNanchangChina
| | - Luyao Chen
- Department of UrologyThe First Affiliated Hospital of Nanchang UniversityNanchangChina
- Jiangxi Institute of UrologyNanchangChina
| | - Songhui Xu
- Department of UrologyThe First Affiliated Hospital of Nanchang UniversityNanchangChina
- Jiangxi Institute of UrologyNanchangChina
| | - Bing Fan
- Department of RadiologyJiangxi Provincial People's Hospital, The First Affiliated Hospital of Nanchang Medical CollegeNanchangChina
| | - Bin Fu
- Department of UrologyThe First Affiliated Hospital of Nanchang UniversityNanchangChina
- Jiangxi Institute of UrologyNanchangChina
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O'Sullivan NJ, Kelly ME. Radiomics and Radiogenomics in Pelvic Oncology: Current Applications and Future Directions. Curr Oncol 2023; 30:4936-4945. [PMID: 37232830 DOI: 10.3390/curroncol30050372] [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: 03/08/2023] [Revised: 04/19/2023] [Accepted: 05/08/2023] [Indexed: 05/27/2023] Open
Abstract
Radiomics refers to the conversion of medical imaging into high-throughput, quantifiable data in order to analyse disease patterns, guide prognosis and aid decision making. Radiogenomics is an extension of radiomics that combines conventional radiomics techniques with molecular analysis in the form of genomic and transcriptomic data, serving as an alternative to costly, labour-intensive genetic testing. Data on radiomics and radiogenomics in the field of pelvic oncology remain novel concepts in the literature. We aim to perform an up-to-date analysis of current applications of radiomics and radiogenomics in the field of pelvic oncology, particularly focusing on the prediction of survival, recurrence and treatment response. Several studies have applied these concepts to colorectal, urological, gynaecological and sarcomatous diseases, with individual efficacy yet poor reproducibility. This article highlights the current applications of radiomics and radiogenomics in pelvic oncology, as well as the current limitations and future directions. Despite a rapid increase in publications investigating the use of radiomics and radiogenomics in pelvic oncology, the current evidence is limited by poor reproducibility and small datasets. In the era of personalised medicine, this novel field of research has significant potential, particularly for predicting prognosis and guiding therapeutic decisions. Future research may provide fundamental data on how we treat this cohort of patients, with the aim of reducing the exposure of high-risk patients to highly morbid procedures.
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Affiliation(s)
- Niall J O'Sullivan
- The Trinity St. James's Cancer Institute, D08 NHY1 Dublin, Ireland
- School of Medicine, Trinity College Dublin, D02 PN40 Dublin, Ireland
| | - Michael E Kelly
- The Trinity St. James's Cancer Institute, D08 NHY1 Dublin, Ireland
- School of Medicine, Trinity College Dublin, D02 PN40 Dublin, Ireland
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Li J, Qiu Z, Cao K, Deng L, Zhang W, Xie C, Yang S, Yue P, Zhong J, Lyu J, Huang X, Zhang K, Zou Y, Huang B. Predicting muscle invasion in bladder cancer based on MRI: A comparison of radiomics, and single-task and multi-task deep learning. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 233:107466. [PMID: 36907040 DOI: 10.1016/j.cmpb.2023.107466] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Revised: 02/09/2023] [Accepted: 03/03/2023] [Indexed: 06/18/2023]
Abstract
BACKGROUND AND OBJECTIVES Radiomics and deep learning are two popular technologies used to develop computer-aided detection and diagnosis schemes for analysing medical images. This study aimed to compare the effectiveness of radiomics, single-task deep learning (DL) and multi-task DL methods in predicting muscle-invasive bladder cancer (MIBC) status based on T2-weighted imaging (T2WI). METHODS A total of 121 tumours (93 for training, from Centre 1; 28 for testing, from Centre 2) were included. MIBC was confirmed with pathological examination. A radiomics model, a single-task model, and a multi-task model based on T2WI were constructed in the training cohort with five-fold cross-validation, and validation was conducted in the external test cohort. Receiver operating characteristic (ROC) curve analysis was performed to evaluate the diagnostic performance of each model. DeLong's test and a permutation test were used to compare the performance of the models. RESULTS The area under the ROC curve (AUC) values of the radiomics, single-task and multi-task models in the training cohort were: 0.920, 0.933 and 0.932, respectively; and were 0.844, 0.884 and 0.932, respectively, in the test cohort. The multi-task model achieved better performance in the test cohort than did the other models. No statistically significant differences in AUC values and Kappa coefficients were observed between pairwise models, in either the training or test cohorts. According to the Grad-CAM feature visualization results, the multi-task model focused more on the diseased tissue area in some samples of the test cohort compared with the single-task model. CONCLUSIONS The T2WI-based radiomics, single-task, and multi-task models all exhibited good diagnostic performance in preoperatively predicting MIBC, in which the multi-task model had the best diagnostic performance. Compared with the radiomics method, our multi-task DL method had the advantage of saving time and effort. Compared with the single-task DL method, our multi-task DL method had the advantage of being more lesion-focused and more reliable for clinical reference.
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Affiliation(s)
- Jianpeng Li
- Department of Radiology, Affiliated Dongguan Hospital, Southern Medical University (Dongguan People's Hospital), Dongguan, Guangdong, China
| | - Zhengxuan Qiu
- Medical AI Lab, School of Biomedical Engineering, Health Science Centre, Shenzhen University, Shenzhen, China
| | - Kangyang Cao
- Medical AI Lab, School of Biomedical Engineering, Health Science Centre, Shenzhen University, Shenzhen, China
| | - Lei Deng
- Department of Radiology, Affiliated Dongguan Hospital, Southern Medical University (Dongguan People's Hospital), Dongguan, Guangdong, China
| | - Weijing Zhang
- Imaging Department, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Chuanmiao Xie
- Imaging Department, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Shuiqing Yang
- Department of Radiology, Affiliated Dongguan Hospital, Southern Medical University (Dongguan People's Hospital), Dongguan, Guangdong, China
| | - Peiyan Yue
- Medical AI Lab, School of Biomedical Engineering, Health Science Centre, Shenzhen University, Shenzhen, China
| | - Jian Zhong
- Medical AI Lab, School of Biomedical Engineering, Health Science Centre, Shenzhen University, Shenzhen, China
| | - Jiegeng Lyu
- Medical AI Lab, School of Biomedical Engineering, Health Science Centre, Shenzhen University, Shenzhen, China
| | - Xiang Huang
- Department of Radiology, Affiliated Dongguan Hospital, Southern Medical University (Dongguan People's Hospital), Dongguan, Guangdong, China
| | - Kunlin Zhang
- Department of Radiology, Affiliated Dongguan Hospital, Southern Medical University (Dongguan People's Hospital), Dongguan, Guangdong, China
| | - Yujian Zou
- Department of Radiology, Affiliated Dongguan Hospital, Southern Medical University (Dongguan People's Hospital), Dongguan, Guangdong, China.
| | - Bingsheng Huang
- Medical AI Lab, School of Biomedical Engineering, Health Science Centre, Shenzhen University, Shenzhen, China.
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15
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Li J, Cao K, Lin H, Deng L, Yang S, Gao Y, Liang M, Lin C, Zhang W, Xie C, Zhang K, Luo J, Pan Z, Yue P, Zou Y, Huang B. Predicting muscle invasion in bladder cancer by deep learning analysis of MRI: comparison with vesical imaging-reporting and data system. Eur Radiol 2023; 33:2699-2709. [PMID: 36434397 DOI: 10.1007/s00330-022-09272-7] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Revised: 09/18/2022] [Accepted: 10/24/2022] [Indexed: 11/27/2022]
Abstract
OBJECTIVES To compare the diagnostic performance of a novel deep learning (DL) method based on T2-weighted imaging with the vesical imaging-reporting and data system (VI-RADS) in predicting muscle invasion in bladder cancer (MIBC). METHODS A total of 215 tumours (129 for training and 31 for internal validation, centre 1; 55 for external validation, centre 2) were included. MIBC was confirmed by pathological examination. VI-RADS scores were provided by two groups of radiologists (readers 1 and readers 2) independently. A deep convolutional neural network was constructed in the training set, and validation was conducted on the internal and external validation sets. ROC analysis was performed to evaluate the performance for MIBC diagnosis. RESULTS The AUCs of the DL model, readers 1, and readers 2 were as follows: in the internal validation set, 0.963, 0.843, and 0.852, respectively; in the external validation set, 0.861, 0.808, and 0.876, respectively. The accuracy of the DL model in the tumours scored VI-RADS 2 or 3 was higher than that of radiologists in the external validation set: for readers 1, 0.886 vs. 0.600, p = 0.006; for readers 2, 0.879 vs. 0.636, p = 0.021. The average processing time (38 s and 43 s in two validation sets) of the DL method was much shorter than the readers, with a reduction of over 100 s in both validation sets. CONCLUSIONS Compared to radiologists using VI-RADS, the DL method had a better diagnostic performance, shorter processing time, and robust generalisability, indicating good potential for diagnosing MIBC. KEY POINTS • The DL model shows robust performance for MIBC diagnosis in both internal and external validation. • The diagnostic performance of the DL model in the tumours scored VI-RADS 2 or 3 is better than that obtained by radiologists using VI-RADS. • The DL method shows potential in the preoperative assessment of MIBC.
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Affiliation(s)
- Jianpeng Li
- Department of Radiology, Affiliated Dongguan Hospital, Southern Medical University, Dongguan, Guangdong, China
| | - Kangyang Cao
- Medical AI Lab, School of Biomedical Engineering, Health Science Centre, Shenzhen University, Shenzhen, China
| | - Hongxin Lin
- Medical AI Lab, School of Biomedical Engineering, Health Science Centre, Shenzhen University, Shenzhen, China
| | - Lei Deng
- Department of Radiology, Affiliated Dongguan Hospital, Southern Medical University, Dongguan, Guangdong, China
| | - Shuiqing Yang
- Department of Radiology, Affiliated Dongguan Hospital, Southern Medical University, Dongguan, Guangdong, China
| | - Yun Gao
- Department of Radiology, Affiliated Dongguan Hospital, Southern Medical University, Dongguan, Guangdong, China
| | - Manqiu Liang
- Department of Radiology, Affiliated Dongguan Hospital, Southern Medical University, Dongguan, Guangdong, China
| | - Chuxuan Lin
- Medical AI Lab, School of Biomedical Engineering, Health Science Centre, Shenzhen University, Shenzhen, China
| | - Weijing Zhang
- Imaging Department, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Chuanmiao Xie
- Imaging Department, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Kunlin Zhang
- Department of Radiology, Affiliated Dongguan Hospital, Southern Medical University, Dongguan, Guangdong, China
| | - Jiexin Luo
- Department of Urology, Affiliated Dongguan Hospital, Southern Medical University, Dongguan, Guangdong, China
| | - Zhaohong Pan
- Medical AI Lab, School of Biomedical Engineering, Health Science Centre, Shenzhen University, Shenzhen, China
| | - Peiyan Yue
- Medical AI Lab, School of Biomedical Engineering, Health Science Centre, Shenzhen University, Shenzhen, China
| | - Yujian Zou
- Department of Radiology, Affiliated Dongguan Hospital, Southern Medical University, Dongguan, Guangdong, China.
| | - Bingsheng Huang
- Medical AI Lab, School of Biomedical Engineering, Health Science Centre, Shenzhen University, Shenzhen, China.
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Chen W, Gong M, Zhou D, Zhang L, Kong J, Jiang F, Feng S, Yuan R. CT-based deep learning radiomics signature for the preoperative prediction of the muscle-invasive status of bladder cancer. Front Oncol 2022; 12:1019749. [PMID: 36544709 PMCID: PMC9761839 DOI: 10.3389/fonc.2022.1019749] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Accepted: 10/17/2022] [Indexed: 12/07/2022] Open
Abstract
Objectives Although the preoperative assessment of whether a bladder cancer (BCa) indicates muscular invasion is crucial for adequate treatment, there currently exist some challenges involved in preoperative diagnosis of BCa with muscular invasion. The aim of this study was to construct deep learning radiomic signature (DLRS) for preoperative predicting the muscle invasion status of BCa. Methods A retrospective review covering 173 patients revealed 43 with pathologically proven muscle-invasive bladder cancer (MIBC) and 130 with non-muscle-invasive bladder cancer (non- MIBC). A total of 129 patients were randomly assigned to the training cohort and 44 to the test cohort. The Pearson correlation coefficient combined with the least absolute shrinkage and selection operator (LASSO) was utilized to reduce radiomic redundancy. To decrease the dimension of deep learning features, Principal Component Analysis (PCA) was adopted. Six machine learning classifiers were finally constructed based on deep learning radiomics features, which were adopted to predict the muscle invasion status of bladder cancer. The area under the curve (AUC), accuracy, sensitivity and specificity were used to evaluate the performance of the model. Results According to the comparison, DLRS-based models performed the best in predicting muscle violation status, with MLP (Train AUC: 0.973260 (95% CI 0.9488-0.9978) and Test AUC: 0.884298 (95% CI 0.7831-0.9855)) outperforming the other models. In the test cohort, the sensitivity, specificity and accuracy of the MLP model were 0.91 (95% CI 0.551-0.873), 0.78 (95% CI 0.594-0.863) and 0.58 (95% CI 0.729-0.827), respectively. DCA indicated that the MLP model showed better clinical utility than Radiomics-only model, which was demonstrated by the decision curve analysis. Conclusions A deep radiomics model constructed with CT images can accurately predict the muscle invasion status of bladder cancer.
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Affiliation(s)
- Weitian Chen
- Department of Urology, Zhongshan People's Hospital, Zhongshan, China
| | - Mancheng Gong
- Department of Urology, Zhongshan People's Hospital, Zhongshan, China
| | - Dongsheng Zhou
- First Clinical Medical College, Guangdong Medical University, Zhanjiang, China
| | - Lijie Zhang
- First Clinical Medical College, Guangdong Medical University, Zhanjiang, China
| | - Jie Kong
- First Clinical Medical College, Guangdong Medical University, Zhanjiang, China
| | - Feng Jiang
- First Clinical Medical College, Guangdong Medical University, Zhanjiang, China
| | - Shengxing Feng
- First Clinical Medical College, Guangdong Medical University, Zhanjiang, China
| | - Runqiang Yuan
- Department of Urology, Zhongshan People's Hospital, Zhongshan, China,*Correspondence: Runqiang Yuan,
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ABDOMEN BECKEN – CT-basierte Radiomics erkennen muskelinvasive Blasenkarzinome. ROFO-FORTSCHR RONTG 2022. [DOI: 10.1055/a-1951-0426] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/03/2022]
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Beşler MS, Koç U. A New Approach to Predict the Histological Variants of Bladder Urothelial Carcinoma: Machine Learning-Based Radiomics Analysis. Acad Radiol 2022; 29:1690-1691. [PMID: 36150963 DOI: 10.1016/j.acra.2022.07.023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2022] [Accepted: 07/28/2022] [Indexed: 11/01/2022]
Affiliation(s)
| | - Ural Koç
- Department of Radiology, Ankara City Hospital, Ankara, Türkiye
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Woźnicki P, Laqua FC, Messmer K, Kunz WG, Stief C, Nörenberg D, Schreier A, Wójcik J, Ruebenthaler J, Ingrisch M, Ricke J, Buchner A, Schulz GB, Gresser E. Radiomics for the Prediction of Overall Survival in Patients with Bladder Cancer Prior to Radical Cystectomy. Cancers (Basel) 2022; 14:4449. [PMID: 36139609 PMCID: PMC9497387 DOI: 10.3390/cancers14184449] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2022] [Revised: 09/06/2022] [Accepted: 09/09/2022] [Indexed: 11/16/2022] Open
Abstract
(1) Background: To evaluate radiomics features as well as a combined model with clinical parameters for predicting overall survival in patients with bladder cancer (BCa). (2) Methods: This retrospective study included 301 BCa patients who received radical cystectomy (RC) and pelvic lymphadenectomy. Radiomics features were extracted from the regions of the primary tumor and pelvic lymph nodes as well as the peritumoral regions in preoperative CT scans. Cross-validation was performed in the training cohort, and a Cox regression model with an elastic net penalty was trained using radiomics features and clinical parameters. The models were evaluated with the time-dependent area under the ROC curve (AUC), Brier score and calibration curves. (3) Results: The median follow-up time was 56 months (95% CI: 48−74 months). In the follow-up period from 1 to 7 years after RC, radiomics models achieved comparable predictive performance to validated clinical parameters with an integrated AUC of 0.771 (95% CI: 0.657−0.869) compared to an integrated AUC of 0.761 (95% CI: 0.617−0.874) for the prediction of overall survival (p = 0.98). A combined clinical and radiomics model stratified patients into high-risk and low-risk groups with significantly different overall survival (p < 0.001). (4) Conclusions: Radiomics features based on preoperative CT scans have prognostic value in predicting overall survival before RC. Therefore, radiomics may guide early clinical decision-making.
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Affiliation(s)
- Piotr Woźnicki
- Department of Diagnostic and Interventional Radiology, University Hospital Würzburg, Würzburg-Oberdürrbacher Str. 6, 97080 Würzburg, Germany
| | - Fabian Christopher Laqua
- Department of Diagnostic and Interventional Radiology, University Hospital Würzburg, Würzburg-Oberdürrbacher Str. 6, 97080 Würzburg, Germany
| | - Katharina Messmer
- Department of Urology, University Hospital, LMU Munich, Munich-Marchioninistr. 15, 81377 Munich, Germany
| | - Wolfgang Gerhard Kunz
- Department of Radiology, University Hospital, LMU Munich, Munich-Marchioninistr. 15, 81377 Munich, Germany
| | - Christian Stief
- Department of Urology, University Hospital, LMU Munich, Munich-Marchioninistr. 15, 81377 Munich, Germany
| | - Dominik Nörenberg
- Department of Radiology and Nuclear Medicine, University Medical Center Mannheim, Mannheim-Theodor-Kutzer-Ufer 1–3, 68167 Mannheim, Germany
| | - Andrea Schreier
- Department of Otolaryngology, University Hospital, LMU Munich, Munich-Marchioninistr. 15, 81377 Munich, Germany
| | - Jan Wójcik
- Faculty of Medicine, Medical University of Warsaw, Żwirki i Wigury 61, 02091 Warsaw, Poland
| | - Johannes Ruebenthaler
- Department of Radiology, University Hospital, LMU Munich, Munich-Marchioninistr. 15, 81377 Munich, Germany
| | - Michael Ingrisch
- Department of Radiology, University Hospital, LMU Munich, Munich-Marchioninistr. 15, 81377 Munich, Germany
| | - Jens Ricke
- Department of Radiology, University Hospital, LMU Munich, Munich-Marchioninistr. 15, 81377 Munich, Germany
| | - Alexander Buchner
- Department of Urology, University Hospital, LMU Munich, Munich-Marchioninistr. 15, 81377 Munich, Germany
| | - Gerald Bastian Schulz
- Department of Urology, University Hospital, LMU Munich, Munich-Marchioninistr. 15, 81377 Munich, Germany
| | - Eva Gresser
- Department of Radiology, University Hospital, LMU Munich, Munich-Marchioninistr. 15, 81377 Munich, Germany
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Qian J, Yang L, Hu S, Gu S, Ye J, Li Z, Du H, Shen H. Feasibility Study on Predicting Recurrence Risk of Bladder Cancer Based on Radiomics Features of Multiphase CT Images. Front Oncol 2022; 12:899897. [PMID: 35719972 PMCID: PMC9201948 DOI: 10.3389/fonc.2022.899897] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2022] [Accepted: 04/27/2022] [Indexed: 12/04/2022] Open
Abstract
Background Predicting the recurrence risk of bladder cancer is crucial for the individualized clinical treatment of patients with bladder cancer. Objective To explore the radiomics based on multiphase CT images combined with clinical risk factors, and to further construct a radiomics-clinical model to predict the recurrence risk of bladder cancer within 2 years after surgery. Methods Patients with bladder cancer who underwent surgical treatment at the First Affiliated Hospital of Soochow University from January 2016 to December 2019 were retrospectively included and followed up to record the disease recurrence. A total of 183 patients were included in the study, and they were randomly divided into training group and validation group in a ratio of 7: 3. The three basic models which are plain scan, corticomedullary phase, and nephrographic phase as well as two combination models, namely, corticomedullary phase + nephrographic phase and plain scan + corticomedullary phase + nephrographic phase, were built with the logistic regression algorithm, and we selected the model with higher performance and calculated the Rad-score (radiomics score) of each patient. The clinical risk factors and Rad-score were screened by Cox univariate and multivariate proportional hazard models in turn to obtain the independent risk factors, then the radiomics-clinical model was constructed, and their performance was evaluated. Results Of the 183 patients included, 128 patients constituted the training group and 55 patients constituted the validation group. In terms of the radiomics-clinical model constructed by three independent risk factors—number of tumors, tumor grade, and Rad-score—the AUCs of the training group and validation group were 0.813 (95% CI 0.740–0.886) and 0.838 (95% CI 0.733–0.943), respectively. In the validation group, the diagnostic accuracy, sensitivity, and specificity were 0.727, 0.739, and 0.719, respectively. Conclusion Combining with radiomics based on multiphase CT images and clinical risk factors, the radiomics-clinical model constructed to predict the recurrence risk of bladder cancer within 2 years after surgery had a good performance.
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Affiliation(s)
- Jing Qian
- Department of Radiology, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Ling Yang
- Department of Radiology, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Su Hu
- Department of Radiology, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Siqian Gu
- Department of Radiology, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Juan Ye
- Department of Radiology, Suzhou Kowloon Hospital Shanghai Jiao Tong University School of Medicine, Suzhou, China
| | - Zhenkai Li
- Department of Radiology, Suzhou Kowloon Hospital Shanghai Jiao Tong University School of Medicine, Suzhou, China
| | - Hongdi Du
- Department of Radiology, Suzhou Kowloon Hospital Shanghai Jiao Tong University School of Medicine, Suzhou, China
| | - Hailin Shen
- Department of Radiology, Suzhou Kowloon Hospital Shanghai Jiao Tong University School of Medicine, Suzhou, China
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