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Cao B, Li Q, Xu P, Zhang Y, Cai S, Rao S, Zeng M, Dai Y, Jiang S, Zhou J. Vesical Imaging-Reporting and Data System (VI-RADS) as a grouping imaging biomarker combined with a decision-tree mode to preoperatively predict the pathological grade of bladder cancer. Clin Radiol 2024; 79:e725-e735. [PMID: 38360514 DOI: 10.1016/j.crad.2024.01.031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Revised: 01/12/2024] [Accepted: 01/22/2024] [Indexed: 02/17/2024]
Abstract
AIM To investigate whether the Vesical Imaging-Reporting and Data System (VI-RADS) could be used to develop a new non-invasive preoperative grade-prediction system to partially predict high-grade bladder cancer (HG-BC). MATERIALS AND METHODS The present study enrolled 89 primary BC patients prospectively from March 2022 to June 2023. Receiver operating characteristic (ROC) curve analysis was performed to evaluate the diagnostic performance of VI-RADS for predicting HG-BC and muscle-invasive bladder cancer (MIBC) in the entire group. In the low VI-RADS (≤2) group, the decision tree-based method was used to obtain significant predictors and construct the decision-tree model (DT model). The performance of the DT model and low VI-RADS scores for predicting HG-BC was determined using ROC, calibration, and decision curve analyses. RESULTS At a cut-off of ≥3, the specificity and positive predictive value of VI-RADS for predicting HG-BC in the entire group was 100%, and the area under the ROC curve (AUC) was 0.697. Among 65 patients with low VI-RADS scores, the DT model showed an AUC of 0.884 in predicting HG-BC compared to 0.506 for low VI-RADS scores. Calibration and decision curve analyses showed that the DT model performed better than the low VI-RADS scores. CONCLUSION Most VI-RADS scores ≥3 correspond to HG-BCs. VI-RADS could be used as a grouping imaging biomarker for a pathological grade-prediction procedure, which in combination with the DT model for low VI-RADS (≤2) populations, would provide a potential preoperative non-invasive method of predicting HG-BC.
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Affiliation(s)
- B Cao
- Department of Radiology, Shanghai Institute of Medical Imaging, Zhongshan Hospital, Fudan University, Shanghai, China; Department of Radiology, Shanghai Geriatric Medical Center, Shanghai, China
| | - Q Li
- Department of Radiology, Shanghai Institute of Medical Imaging, Zhongshan Hospital, Fudan University, Shanghai, China
| | - P Xu
- Department of Urology, Xuhui Hospital, Fudan University, Shanghai, China
| | - Y Zhang
- MR Collaboration, Central Research Institute, United Imaging Healthcare, Shanghai, China
| | - S Cai
- Department of Radiology, Shanghai Institute of Medical Imaging, Zhongshan Hospital, Fudan University, Shanghai, China
| | - S Rao
- Department of Radiology, Shanghai Institute of Medical Imaging, Zhongshan Hospital, Fudan University, Shanghai, China; Department of Radiology, Shanghai Geriatric Medical Center, Shanghai, China
| | - M Zeng
- Department of Radiology, Shanghai Institute of Medical Imaging, Zhongshan Hospital, Fudan University, Shanghai, China; Department of Radiology, Shanghai Geriatric Medical Center, Shanghai, China
| | - Y Dai
- MR Collaboration, Central Research Institute, United Imaging Healthcare, Shanghai, China
| | - S Jiang
- Department of Urology, Zhongshan Hospital, Fudan University, Shanghai, China; Department of Urology, Zhongshan Hospital Wusong Branch, Fudan University, Shanghai, China.
| | - J Zhou
- Department of Radiology, Fudan University Zhongshan Hospital Xiamen Branch, Xiamen, China; Xiamen Municipal Clinical Research Center for Medical Imaging, Xiamen, China; Xiamen Key Clinical Specialty for Radiology, Xiamen, China.
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He K, Meng X, Wang Y, Feng C, Liu Z, Li Z, Niu Y. Progress of Multiparameter Magnetic Resonance Imaging in Bladder Cancer: A Comprehensive Literature Review. Diagnostics (Basel) 2024; 14:442. [PMID: 38396481 PMCID: PMC10888296 DOI: 10.3390/diagnostics14040442] [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: 12/21/2023] [Revised: 01/25/2024] [Accepted: 02/13/2024] [Indexed: 02/25/2024] Open
Abstract
Magnetic resonance imaging (MRI) has been proven to be an indispensable imaging method in bladder cancer, and it can accurately identify muscular invasion of bladder cancer. Multiparameter MRI is a promising tool widely used for preoperative staging evaluation of bladder cancer. Vesical Imaging-Reporting and Data System (VI-RADS) scoring has proven to be a reliable tool for local staging of bladder cancer with high accuracy in preoperative staging, but VI-RADS still faces challenges and needs further improvement. Artificial intelligence (AI) holds great promise in improving the accuracy of diagnosis and predicting the prognosis of bladder cancer. Automated machine learning techniques based on radiomics features derived from MRI have been utilized in bladder cancer diagnosis and have demonstrated promising potential for practical implementation. Future work should focus on conducting more prospective, multicenter studies to validate the additional value of quantitative studies and optimize prediction models by combining other biomarkers, such as urine and serum biomarkers. This review assesses the value of multiparameter MRI in the accurate evaluation of muscular invasion of bladder cancer, as well as the current status and progress of its application in the evaluation of efficacy and prognosis.
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Affiliation(s)
- Kangwen He
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China (X.M.); (Z.L.)
| | - Xiaoyan Meng
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China (X.M.); (Z.L.)
| | - Yanchun Wang
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China (X.M.); (Z.L.)
| | - Cui Feng
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China (X.M.); (Z.L.)
| | - Zheng Liu
- Department of Urology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Zhen Li
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China (X.M.); (Z.L.)
| | - Yonghua Niu
- Department of Pediatric Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
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Kong L, Wen Z, Cai Q, Lin Y, Chen Y, Cao W, Li M, Qian L, Chen J, Guo Y, Wang H. Amide Proton Transfer-Weighted MRI and Diffusion-Weighted Imaging in Bladder Cancer: A Complementary Tool to the VI-RADS. Acad Radiol 2024; 31:564-571. [PMID: 37821347 DOI: 10.1016/j.acra.2023.09.005] [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: 08/08/2023] [Revised: 08/27/2023] [Accepted: 09/03/2023] [Indexed: 10/13/2023]
Abstract
RATIONALE AND OBJECTIVES To investigate the feasibility of amide proton transfer-weighted (APTw) and diffusion-weighted Magnetic Resonance Imaging (MRI) as a means by which to add value to the Vesical Imaging Reporting and Data System (VI-RADS) for discriminating muscle invasive bladder cancer (MIBC) from nonmuscle invasive bladder cancer (NMIBC). MATERIALS AND METHODS This prospective study enrolled participants with pathologically confirmed bladder cancer (BCa) who underwent preoperative multiparametric MRI, including APTw and diffusion-weighted MRI, from July 2020 to January 2023. The exclusion criteria were lesions smaller than 10 mm, missing smooth muscle layer in the operation specimen, neoadjuvant therapy before MRI, inadequate image quality, and malignancy other than urothelial neoplasm. Two radiologists independently assigned the VI-RADS score for each participant. Quantitative parameters derived from APTw and diffusion-weighted MRI were obtained by another two radiologists. Receiver operating characteristic (ROC) curve analysis with the area under the ROC curve (AUC) was performed to evaluate the diagnostic performances of quantitative parameters for discriminating BCa detrusor muscle invasion status. RESULTS A total of 106 participants were enrolled (mean age, 64 ± 12 years [SD]; 90 men): 32 with MIBC and 74 with NMIBC. Lower apparent diffusion coefficient (ADC) values (0.88 × 10-3 mm2/s ± 0.12 vs. 1.08 × 10-3 mm2/s ± 0.25; P < 0.001) and higher APTw values (6.89% [interquartile range {IQR}, 5.05%-12.17%] vs. 3.61% [IQR, 2.23%-6.83%]; P < 0.001) were observed in the MIBC group. Compared to VI-RADS alone, both APTw (P = 0.003) and ADC (P = 0.020) values could improve the diagnostic performance of VI-RADS in differentiating MIBC from NMIBC. The combination of the three yielded the highest diagnostic performance (AUC, 0.93; 95% CI:0.87,0.97) for evaluating muscle invasion status. The addition of the APTw values to the combination of VI-RADS and ADC values notably improved the diagnostic performance for differentiating NMIBC from MIBC (VI-RADS+ADC vs. VI-RADS+APTw+ADC, P = 0.046). CONCLUSION MRI parameters derived from APTw and diffusion-weighted MRI can be used to accurately assess muscle invasion status in BCa and provide additional value to VI-RADS.
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Affiliation(s)
- Lingmin Kong
- Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, PR China (L.K., Z.W., Q.C., Y.L., Y.C., W.C., M.L., Y.G., H.W.)
| | - Zhihua Wen
- Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, PR China (L.K., Z.W., Q.C., Y.L., Y.C., W.C., M.L., Y.G., H.W.)
| | - Qian Cai
- Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, PR China (L.K., Z.W., Q.C., Y.L., Y.C., W.C., M.L., Y.G., H.W.)
| | - Yingyu Lin
- Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, PR China (L.K., Z.W., Q.C., Y.L., Y.C., W.C., M.L., Y.G., H.W.)
| | - Yanling Chen
- Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, PR China (L.K., Z.W., Q.C., Y.L., Y.C., W.C., M.L., Y.G., H.W.)
| | - Wenxin Cao
- Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, PR China (L.K., Z.W., Q.C., Y.L., Y.C., W.C., M.L., Y.G., H.W.)
| | - Meiqin Li
- Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, PR China (L.K., Z.W., Q.C., Y.L., Y.C., W.C., M.L., Y.G., H.W.)
| | - Long Qian
- MR Research, GE Healthcare, Beijing, China (L.Q.)
| | - Junxing Chen
- Department of Urology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, PR China (J.C.)
| | - Yan Guo
- Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, PR China (L.K., Z.W., Q.C., Y.L., Y.C., W.C., M.L., Y.G., H.W.)
| | - Huanjun Wang
- Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, PR China (L.K., Z.W., Q.C., Y.L., Y.C., W.C., M.L., Y.G., H.W.).
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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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Boca B, Caraiani C, Telecan T, Pintican R, Lebovici A, Andras I, Crisan N, Pavel A, Diosan L, Balint Z, Lupsor-Platon M, Buruian MM. MRI-Based Radiomics in Bladder Cancer: A Systematic Review and Radiomics Quality Score Assessment. Diagnostics (Basel) 2023; 13:2300. [PMID: 37443692 DOI: 10.3390/diagnostics13132300] [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: 05/22/2023] [Revised: 06/30/2023] [Accepted: 07/03/2023] [Indexed: 07/15/2023] Open
Abstract
(1): Background: With the recent introduction of vesical imaging reporting and data system (VI-RADS), magnetic resonance imaging (MRI) has become the main imaging method used for the preoperative local staging of bladder cancer (BCa). However, the VI-RADS score is subject to interobserver variability and cannot provide information about tumor cellularity. These limitations may be overcome by using a quantitative approach, such as the new emerging domain of radiomics. (2) Aim: To systematically review published studies on the use of MRI-based radiomics in bladder cancer. (3) Materials and Methods: We performed literature research using the PubMed MEDLINE, Scopus, and Web of Science databases using PRISMA principles. A total of 1092 papers that addressed the use of radiomics for BC staging, grading, and treatment response were retrieved using the keywords "bladder cancer", "magnetic resonance imaging", "radiomics", and "textural analysis". (4) Results: 26 papers met the eligibility criteria and were included in the final review. The principal applications of radiomics were preoperative tumor staging (n = 13), preoperative prediction of tumor grade or molecular correlates (n = 9), and prediction of prognosis/response to neoadjuvant therapy (n = 4). Most of the developed radiomics models included second-order features mainly derived from filtered images. These models were validated in 16 studies. The average radiomics quality score was 11.7, ranging between 8.33% and 52.77%. (5) Conclusions: MRI-based radiomics holds promise as a quantitative imaging biomarker of BCa characterization and prognosis. However, there is still need for improving the standardization of image preprocessing, feature extraction, and external validation before applying radiomics models in the clinical setting.
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Affiliation(s)
- Bianca Boca
- Department of Radiology, "George Emil Palade", University of Medicine, Pharmacy, Science and Technology of Targu Mures, 540139 Targu Mures, Romania
- Department of Medical Imaging and Nuclear Medicine, "Iuliu Hațieganu" University of Medicine and Pharmacy Cluj-Napoca, 400012 Cluj-Napoca, Romania
| | - Cosmin Caraiani
- Department of Medical Imaging and Nuclear Medicine, "Iuliu Hațieganu" University of Medicine and Pharmacy Cluj-Napoca, 400012 Cluj-Napoca, Romania
| | - Teodora Telecan
- Department of Urology, "Iuliu Hațieganu" University of Medicine and Pharmacy Cluj-Napoca, 400012 Cluj-Napoca, Romania
- Department of Urology, Clinical Municipal Hospital, 400139 Cluj-Napoca, Romania
| | - Roxana Pintican
- Department of Radiology, "Iuliu Hațieganu" University of Medicine and Pharmacy Cluj-Napoca, 400012 Cluj-Napoca, Romania
| | - Andrei Lebovici
- Department of Radiology, "Iuliu Hațieganu" University of Medicine and Pharmacy Cluj-Napoca, 400012 Cluj-Napoca, Romania
| | - Iulia Andras
- Department of Urology, "Iuliu Hațieganu" University of Medicine and Pharmacy Cluj-Napoca, 400012 Cluj-Napoca, Romania
- Department of Urology, Clinical Municipal Hospital, 400139 Cluj-Napoca, Romania
| | - Nicolae Crisan
- Department of Urology, "Iuliu Hațieganu" University of Medicine and Pharmacy Cluj-Napoca, 400012 Cluj-Napoca, Romania
- Department of Urology, Clinical Municipal Hospital, 400139 Cluj-Napoca, Romania
| | - Alexandru Pavel
- Department of Radiology, "George Emil Palade", University of Medicine, Pharmacy, Science and Technology of Targu Mures, 540139 Targu Mures, Romania
| | - Laura Diosan
- Department of Computer Science, Faculty of Mathematics and Computer Science, "Babes-Bolyai" University, 400157 Cluj-Napoca, Romania
| | - Zoltan Balint
- Department of Biomedical Physics, Faculty of Physics, "Babes-Bolyai" University, 400084 Cluj-Napoca, Romania
| | - Monica Lupsor-Platon
- Department of Medical Imaging and Nuclear Medicine, "Iuliu Hațieganu" University of Medicine and Pharmacy Cluj-Napoca, 400012 Cluj-Napoca, Romania
- Department of Radiology, Regional Institute of Gastroenterology and Hepatology "Prof. Dr. Octavian Fodor", 400162 Cluj-Napoca, Romania
| | - Mircea Marian Buruian
- Department of Radiology, "George Emil Palade", University of Medicine, Pharmacy, Science and Technology of Targu Mures, 540139 Targu Mures, Romania
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