{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,9]],"date-time":"2026-10-09T20:39:51Z","timestamp":1791578391358,"version":"4.3.4"},"reference-count":98,"publisher":"JMIR Publications Inc.","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J Med Internet Res"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec sec-type=\"background\">\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Some researchers have explored the application of radiomics-based machine learning to detect preoperative muscle invasion, high-grade tumors, human epidermal growth factor receptor 2 expression, and other risk factors for bladder cancer. However, systematic evidence proving its effectiveness remains lacking.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec sec-type=\"objective\">\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>This study aimed to evaluate the performance of radiomics-based machine learning in preoperative risk stratification for patients with bladder cancer. These findings could contribute to advancing the development or updating of intelligent risk assessment tools for bladder cancer.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec sec-type=\"methods\">\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>The Embase, Cochrane Library, PubMed, and Web of Science databases were systematically retrieved for publicly available studies on the effectiveness of radiomics-based machine learning (ML) in the preoperative risk stratification of bladder cancer up to October 17, 2025. The risk of bias in the included studies was evaluated using the Prediction Model Risk of Bias Assessment Tool for Artificial Intelligence. The overall quality of the studies was quantified using the Radiomics Quality Scoring tool. The certainty of the evidence was graded using the Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) framework. Subgroup analyses were conducted according to the type of imaging source and modeling method.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec sec-type=\"results\">\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>This meta-analysis ultimately incorporated 57 studies with a total of 11,933 participants. These studies primarily used radiomics-based ML to identify muscle invasion (n=34) and high-grade tumors (n=16). Additionally, the methodology was used to evaluate human epidermal growth factor receptor 2 positive expression (n=3), Ki-67 expression (n=2), and lymph node staging (n=2) preoperatively in bladder cancer. In the validation sets, the pooled area under the receiver operating characteristic curve (AUROC) for identifying muscle invasion was 0.893 (95% CI 0.840-0.948), 0.916 (95% CI 0.891-0.942), and 0.840 (95% CI 0.737-0.958) for computed tomography (CT)\u2013, magnetic resonance imaging (MRI)\u2013, and ultrasound-based radiomics, respectively. The AUROC was 0.874 (95% CI 0.852-0.896) and 0.921 (95% CI 0.867-0.979) for models integrating clinical features with CT- or MRI-based radiomics, respectively. The pooled AUROC for diagnosing high-grade tumors was 0.874 (95% CI 0.775-0.985), 0.846 (95% CI 0.663-1.000), and 0.750 (95% CI 0.636-0.884) for CT-, MRI-, and ultrasound-based radiomics, respectively. Furthermore, the AUROC was 0.919 (95% CI 0.774-1.000) for MRI-based radiomics combined with clinical features.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec sec-type=\"conclusions\">\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>This is the first systematic review to comprehensively evaluate the role of radiomics in preoperative risk stratification for bladder cancer. It provides evidence to inform the development and refinement of future ML-based tools for image analysis in this setting. However, this evidence faces significant challenges, including methodological shortcomings and a high risk of bias and low GRADE level, which preclude its readiness for clinical translation. Future studies should standardize the methodological workflows in radiomics, conduct multicenter research, and thoroughly evaluate and discuss the validity of external validation.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.2196\/81084","type":"journal-article","created":{"date-parts":[[2026,6,12]],"date-time":"2026-06-12T22:15:36Z","timestamp":1781302536000},"page":"e81084-e81084","source":"Crossref","is-referenced-by-count":1,"title":["Application Value of Radiomics-Based Machine Learning for Preoperative Risk Stratification of Bladder Cancer: Systematic Review and Meta-Analysis"],"prefix":"10.2196","volume":"28","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-1304-9094","authenticated-orcid":false,"given":"Zirong","family":"He","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4022-1249","authenticated-orcid":false,"given":"Yinghua","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8826-9052","authenticated-orcid":false,"given":"Qin","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2463-7087","authenticated-orcid":false,"given":"Fang","family":"Hou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8933-6402","authenticated-orcid":false,"given":"Bing","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1010","published-online":{"date-parts":[[2026,6,12]]},"reference":[{"issue":"3","key":"R1","doi-asserted-by":"publisher","first-page":"209","DOI":"10.3322\/caac.21660","article-title":"Global Cancer Statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries","volume":"71","author":"Sung","journal-title":"CA Cancer J Clin"},{"issue":"1","key":"R2","doi-asserted-by":"publisher","first-page":"12","DOI":"10.3322\/caac.21820","article-title":"Cancer statistics, 2024","volume":"74","author":"Siegel","journal-title":"CA Cancer J Clin"},{"issue":"1","key":"R3","doi-asserted-by":"publisher","first-page":"75","DOI":"10.1016\/j.eururo.2021.08.010","article-title":"European Association of Urology Guidelines on non-muscle-invasive bladder cancer (Ta, T1, and carcinoma in situ)","volume":"81","author":"Babjuk","journal-title":"Eur Urol"},{"issue":"5","key":"R4","doi-asserted-by":"publisher","first-page":"843","DOI":"10.1016\/j.eururo.2009.05.047","article-title":"Detrusor muscle in the first, apparently complete transurethral resection of bladder tumour specimen is a surrogate marker of resection quality, predicts risk of early recurrence, and is dependent on operator experience","volume":"57","author":"Mariappan","journal-title":"Eur Urol"},{"issue":"4","key":"R5","doi-asserted-by":"publisher","first-page":"978","DOI":"10.1016\/j.jsurg.2020.02.010","article-title":"The learning curve for transurethral resection of bladder tumour: how many is enough to be independent, safe and effective surgeon?","volume":"77","author":"Poletajew","journal-title":"J Surg Educ"},{"issue":"6","key":"R6","doi-asserted-by":"publisher","first-page":"898","DOI":"10.1111\/j.1464-410X.2010.09628.x","article-title":"Discrepancy between clinical and pathological stage: external validation of the impact on prognosis in an international radical cystectomy cohort","volume":"107","author":"Svatek","journal-title":"BJU Int"},{"issue":"1","key":"R7","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1016\/j.euf.2023.07.002","article-title":"Repeat transurethral resection for non-muscle-invasive bladder cancer: an updated systematic review and meta-analysis in the contemporary era","volume":"10","author":"Yanagisawa","journal-title":"Eur Urol Focus"},{"issue":"7","key":"R8","doi-asserted-by":"publisher","first-page":"3084","DOI":"10.1007\/s00261-024-04786-8","article-title":"Precise vesical wall staging of bladder cancer in the era of precision medicine: has it been fulfilled?","volume":"50","author":"Nouh","journal-title":"Abdom Radiol (NY)"},{"issue":"1","key":"R9","doi-asserted-by":"publisher","DOI":"10.3390\/cancers16010160","article-title":"Cystoscopy accuracy in detecting bladder tumors: a prospective video-confirmed study","volume":"16","author":"Guldhammer","journal-title":"Cancers (Basel)"},{"issue":"3","key":"R10","doi-asserted-by":"publisher","first-page":"728","DOI":"10.1016\/j.euf.2021.05.005","article-title":"Role of radiomics in the prediction of muscle-invasive bladder cancer: a systematic review and meta-analysis","volume":"8","author":"Kozikowski","journal-title":"Eur Urol Focus"},{"issue":"2","key":"R11","doi-asserted-by":"publisher","first-page":"563","DOI":"10.1148\/radiol.2015151169","article-title":"Radiomics: images are more than pictures, they are data","volume":"278","author":"Gillies","journal-title":"Radiology"},{"issue":"1","key":"R12","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-024-01088-7","article-title":"Predicting non-muscle invasive bladder cancer outcomes using artificial intelligence: a systematic review using APPRAISE-AI","volume":"7","author":"Kwong","journal-title":"NPJ Digit Med"},{"issue":"1","key":"R13","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1097\/MOU.0000000000001144","article-title":"AI-powered radiomics: revolutionizing detection of urologic malignancies","volume":"34","author":"Gelikman","journal-title":"Curr Opin Urol"},{"issue":"13","key":"R14","doi-asserted-by":"publisher","DOI":"10.3390\/diagnostics13132300","article-title":"MRI-based radiomics in bladder cancer: a systematic review and radiomics quality score assessment","volume":"13","author":"Boca","journal-title":"Diagnostics (Basel)"},{"issue":"10","key":"R15","doi-asserted-by":"publisher","first-page":"6241","DOI":"10.1007\/s00330-024-10699-3","article-title":"Can we predict pathology without surgery? Weighing the added value of multiparametric MRI and whole prostate radiomics in integrative machine learning models","volume":"34","author":"Marvaso","journal-title":"Eur Radiol"},{"issue":"1","key":"R16","doi-asserted-by":"publisher","first-page":"62","DOI":"10.1016\/j.acra.2019.10.001","article-title":"Artificial intelligence in medicine: where are we now?","volume":"27","author":"Kulkarni","journal-title":"Acad Radiol"},{"issue":"3","key":"R17","doi-asserted-by":"publisher","first-page":"202","DOI":"10.4111\/icu.20230435","article-title":"Applications of artificial intelligence in urologic oncology","volume":"65","author":"Pak","journal-title":"Investig Clin Urol"},{"issue":"12","key":"R18","doi-asserted-by":"publisher","first-page":"7215","DOI":"10.1016\/j.acra.2025.08.035","article-title":"AI-driven and traditional radiomic model for predicting muscle invasion in bladder cancer via multi-parametric imaging: a systematic review and meta-analysis","volume":"32","author":"Wang","journal-title":"Acad Radiol"},{"issue":"1","key":"R19","doi-asserted-by":"publisher","DOI":"10.1186\/s13244-024-01780-y","article-title":"The accuracy and quality of image-based artificial intelligence for muscle-invasive bladder cancer prediction","volume":"15","author":"He","journal-title":"Insights Imaging"},{"issue":"5","key":"R20","doi-asserted-by":"publisher","first-page":"543","DOI":"10.1097\/MOU.0000000000001309","article-title":"Artificial intelligence in muscle-invasive bladder cancer: opportunities, challenges, and clinical impact","volume":"35","author":"Mastroleo","journal-title":"Curr Opin Urol"},{"issue":"2","key":"R21","doi-asserted-by":"publisher","first-page":"165","DOI":"10.1097\/MOU.0000000000001249","article-title":"The role of MRI in muscle-invasive bladder cancer: an update from the last two years","volume":"35","author":"Pastorino","journal-title":"Curr Opin Urol"},{"issue":"1","key":"R22","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-022-00613-w","article-title":"Prostate cancer therapy personalization via multi-modal deep learning on randomized phase III clinical trials","volume":"5","author":"Esteva","journal-title":"NPJ Digit Med"},{"issue":"7","key":"R23","doi-asserted-by":"publisher","DOI":"10.1136\/bmjopen-2020-048008","article-title":"Protocol for development of a reporting guideline (TRIPOD-AI) and risk of bias tool (PROBAST-AI) for diagnostic and prognostic prediction model studies based on artificial intelligence","volume":"11","author":"Collins","journal-title":"BMJ Open"},{"key":"R24","doi-asserted-by":"publisher","DOI":"10.1136\/bmj-2024-082505","article-title":"PROBAST+AI: an updated quality, risk of bias, and applicability assessment tool for prediction models using regression or artificial intelligence methods","volume":"388","author":"Moons","journal-title":"BMJ"},{"issue":"12","key":"R25","doi-asserted-by":"publisher","first-page":"749","DOI":"10.1038\/nrclinonc.2017.141","article-title":"Radiomics: the bridge between medical imaging and personalized medicine","volume":"14","author":"Lambin","journal-title":"Nat Rev Clin Oncol"},{"issue":"9","key":"R26","doi-asserted-by":"publisher","first-page":"2768","DOI":"10.1177\/0962280218785504","article-title":"A framework for meta-analysis of prediction model studies with binary and time-to-event outcomes","volume":"28","author":"Debray","journal-title":"Stat Methods Med Res"},{"key":"R27","doi-asserted-by":"publisher","DOI":"10.1186\/1471-2288-14-25","article-title":"The Hartung-Knapp-Sidik-Jonkman method for random effects meta-analysis is straightforward and considerably outperforms the standard DerSimonian-Laird method","volume":"14","author":"IntHout","journal-title":"BMC Med Res Methodol"},{"issue":"1","key":"R28","doi-asserted-by":"publisher","DOI":"10.1186\/s12880-024-01276-7","article-title":"Predicting preoperative muscle invasion status for bladder cancer using computed tomography-based radiomics nomogram","volume":"24","author":"Zhang","journal-title":"BMC Med Imaging"},{"key":"R29","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2021.654685","article-title":"Deep learning on enhanced CT images can predict the muscular invasiveness of bladder cancer","volume":"11","author":"Zhang","journal-title":"Front Oncol"},{"issue":"12","key":"R30","doi-asserted-by":"publisher","DOI":"10.3390\/bioengineering10121355","article-title":"Radiomics prediction of muscle invasion in bladder cancer using semi-automatic lesion segmentation of MRI compared with manual segmentation","volume":"10","author":"Ye","journal-title":"Bioengineering (Basel)"},{"issue":"1","key":"R31","doi-asserted-by":"publisher","first-page":"131","DOI":"10.1186\/s43055-023-01044-7","article-title":"Preoperative CT-based radiomics for diagnosing muscle invasion of bladder cancer","volume":"54","author":"Ren","journal-title":"Egypt J Radiol Nucl Med"},{"issue":"1","key":"R32","doi-asserted-by":"publisher","DOI":"10.3390\/ijms25010088","article-title":"MRI\/RNA-seq-based radiogenomics and artificial intelligence for more accurate staging of muscle-invasive bladder cancer","volume":"25","author":"Qureshi","journal-title":"Int J Mol Sci"},{"issue":"9","key":"R33","doi-asserted-by":"publisher","DOI":"10.7759\/cureus.45488","article-title":"Can only the shape feature in radiomics help machine learning show that bladder cancer has invaded muscles?","volume":"15","author":"\u00d6zdemir","journal-title":"Cureus"},{"key":"R34","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2022.1019749","article-title":"CT-based deep learning radiomics signature for the preoperative prediction of the muscle-invasive status of bladder cancer","volume":"12","author":"Chen","journal-title":"Front Oncol"},{"issue":"1","key":"R35","doi-asserted-by":"publisher","DOI":"10.1186\/s40644-021-00433-3","article-title":"Magnetic resonance imaging-based radiomics signature for preoperative prediction of Ki67 expression in bladder cancer","volume":"21","author":"Zheng","journal-title":"Cancer Imaging"},{"issue":"18","key":"R36","doi-asserted-by":"publisher","DOI":"10.3390\/jcm12185900","article-title":"Radiomic analysis of quantitative T2 mapping and conventional MRI in predicting histologic grade of bladder cancer","volume":"12","author":"Ye","journal-title":"J Clin Med"},{"issue":"5","key":"R37","doi-asserted-by":"publisher","DOI":"10.3390\/jpm12050726","article-title":"Optimization of preoperative lymph node staging in patients with muscle-invasive bladder cancer using radiomics on computed tomography","volume":"12","author":"Starmans","journal-title":"J Pers Med"},{"issue":"6","key":"R38","doi-asserted-by":"publisher","DOI":"10.3390\/cancers15061673","article-title":"Performing automatic identification and staging of urothelial carcinoma in bladder cancer patients using a hybrid deep-machine learning approach","volume":"15","author":"Sarkar","journal-title":"Cancers (Basel)"},{"key":"R39","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2023.1025972","article-title":"An MRI-based radiomics nomogram in predicting histologic grade of non-muscle-invasive bladder cancer","volume":"13","author":"Li","journal-title":"Front Oncol"},{"key":"R40","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2023.1166245","article-title":"Machine learning models combining computed tomography semantic features and selected clinical variables for accurate prediction of the pathological grade of bladder cancer","volume":"13","author":"Deng","journal-title":"Front Oncol"},{"issue":"12","key":"R41","doi-asserted-by":"publisher","first-page":"8821","DOI":"10.1007\/s00330-023-09960-y","article-title":"A machine learning model based on MRI for the preoperative prediction of bladder cancer invasion depth","volume":"33","author":"Chen","journal-title":"Eur Radiol"},{"issue":"5","key":"R42","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6560\/ad25c7","article-title":"A novel predict method for muscular invasion of bladder cancer based on 3D mp-MRI feature fusion","volume":"69","author":"Yu","journal-title":"Phys Med Biol"},{"issue":"3","key":"R43","doi-asserted-by":"publisher","first-page":"1804","DOI":"10.1007\/s00330-019-06484-2","article-title":"Combining DWI radiomics features with transurethral resection promotes the differentiation between muscle-invasive bladder cancer and non-muscle-invasive bladder cancer","volume":"30","author":"Xu","journal-title":"Eur Radiol"},{"key":"R44","doi-asserted-by":"crossref","unstructured":"Zhou Z Liu L Xue K Ma Y Liu J Zhang M . Assessment of pathological grading of bladder cancer using texture features from MRI. Presented at: 2019 IEEE International Conference on Mechatronics and Automation (ICMA); Aug 4-7, 2019:1333-1337; Tianjin, China. 2019.URL: https:\/\/ieeexplore.ieee.org\/xpl\/mostRecentIssue.jsp?punumber=8801712 [doi: 10.1109\/ICMA.2019.8816242]","DOI":"10.1109\/ICMA.2019.8816242"},{"key":"R45","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejrad.2021.109666","article-title":"Application of deep learning as a noninvasive tool to differentiate muscle-invasive bladder cancer and non-muscle-invasive bladder cancer with CT","volume":"139","author":"Yang","journal-title":"Eur J Radiol"},{"key":"R46","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2021.619893","article-title":"Combining Multiparametric MRI Radiomics Signature With the Vesical Imaging-Reporting and Data System (VI-RADS) Score to Preoperatively Differentiate Muscle Invasion of Bladder Cancer","volume":"11","author":"Zheng","journal-title":"Front Oncol"},{"issue":"1","key":"R47","doi-asserted-by":"publisher","first-page":"89","DOI":"10.1186\/s40644-023-00609-z","article-title":"CT-based deep learning radiomics nomogram for the prediction of pathological grade in bladder cancer: a multicenter study","volume":"23","author":"Song","journal-title":"Cancer Imaging"},{"issue":"6","key":"R48","doi-asserted-by":"publisher","first-page":"e473","DOI":"10.1016\/j.crad.2022.02.019","article-title":"CT-based radiomics for the preoperative prediction of the muscle-invasive status of bladder cancer and comparison to radiologists\u2019 assessment","volume":"77","author":"Cui","journal-title":"Clin Radiol"},{"issue":"12","key":"R49","doi-asserted-by":"publisher","first-page":"6749","DOI":"10.1007\/s00330-020-06893-8","article-title":"CT-based radiomics to predict the pathological grade of bladder cancer","volume":"30","author":"Zhang","journal-title":"Eur Radiol"},{"issue":"2","key":"R50","doi-asserted-by":"publisher","DOI":"10.1016\/j.heliyon.2024.e24878","article-title":"Development and validation of a CT-based deep learning radiomics nomogram to predict muscle invasion in bladder cancer","volume":"10","author":"Wei","journal-title":"Heliyon"},{"issue":"24","key":"R51","doi-asserted-by":"publisher","first-page":"4388","DOI":"10.1002\/cncr.32490","article-title":"Development of a noninvasive tool to preoperatively evaluate the muscular invasiveness of bladder cancer using a radiomics approach","volume":"125","author":"Zheng","journal-title":"Cancer"},{"issue":"9","key":"R52","doi-asserted-by":"publisher","first-page":"4816","DOI":"10.1007\/s00330-020-06796-8","article-title":"Elaboration of a multisequence MRI-based radiomics signature for the preoperative prediction of the muscle-invasive status of bladder cancer: a double-center study","volume":"30","author":"Wang","journal-title":"Eur Radiol"},{"issue":"9","key":"R53","doi-asserted-by":"publisher","first-page":"4311","DOI":"10.1007\/s00261-021-03108-6","article-title":"Integrating multiparametric MRI radiomics features and the Vesical Imaging-Reporting and Data System (VI-RADS) for bladder cancer grading","volume":"46","author":"Zheng","journal-title":"Abdom Radiol (NY)"},{"issue":"6","key":"R54","doi-asserted-by":"publisher","first-page":"294","DOI":"10.1016\/j.urolonc.2022.10.024","article-title":"Integrating radiomics with the vesical imaging-reporting and data system to predict muscle invasion of bladder cancer","volume":"41","author":"Wang","journal-title":"Urol Oncol"},{"issue":"6","key":"R55","doi-asserted-by":"publisher","first-page":"2694","DOI":"10.1002\/jmri.29342","article-title":"MRI-based machine learning radiomics for preoperative assessment of human epidermal growth factor receptor 2 status in urothelial bladder carcinoma","volume":"60","author":"Yu","journal-title":"J Magn Reson Imaging"},{"issue":"11","key":"R56","doi-asserted-by":"publisher","first-page":"e889","DOI":"10.1016\/j.crad.2023.07.020","article-title":"MRI-based radiomics analysis of bladder cancer: prediction of pathological grade and histological variant","volume":"78","author":"Wang","journal-title":"Clin Radiol"},{"issue":"1","key":"R57","doi-asserted-by":"publisher","first-page":"258","DOI":"10.1002\/jmri.28498","article-title":"Multi\u2010sequence and multi\u2010regional MRI \u2010based radiomics nomogram for the preoperative assessment of muscle invasion in bladder cancer","volume":"58","author":"Zhang","journal-title":"J Magn Reson Imaging"},{"issue":"Pt A","key":"R58","doi-asserted-by":"publisher","first-page":"106219","DOI":"10.1016\/j.compbiomed.2022.106219","article-title":"Multi-task deep learning based on T2-weighted images for predicting muscular-invasive bladder cancer","volume":"151","author":"Zou","journal-title":"Comput Biol Med"},{"key":"R59","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymeth.2022.08.010","article-title":"PENet: Prior evidence deep neural network for bladder cancer staging","volume":"207","author":"Zhou","journal-title":"Methods"},{"key":"R60","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2023.107466","article-title":"Predicting muscle invasion in bladder cancer based on MRI: a comparison of radiomics, and single-task and multi-task deep learning","volume":"233","author":"Li","journal-title":"Comput Methods Programs Biomed"},{"issue":"6","key":"R61","doi-asserted-by":"publisher","first-page":"176","DOI":"10.1016\/j.urolonc.2024.02.008","article-title":"Prediction of histopathologic grades of bladder cancer with radiomics based on MRI: comparison with traditional MRI","volume":"42","author":"Li","journal-title":"Urol Oncol"},{"issue":"2","key":"R62","doi-asserted-by":"publisher","first-page":"659","DOI":"10.1007\/s00261-022-03730-y","article-title":"Preoperative CT features to predict risk stratification of non-muscle invasive bladder cancer","volume":"48","author":"Chen","journal-title":"Abdom Radiol (NY)"},{"issue":"7","key":"R63","doi-asserted-by":"publisher","first-page":"1896","DOI":"10.1007\/s00261-017-1079-6","article-title":"Preoperative prediction of muscular invasiveness of bladder cancer with radiomic features on conventional MRI and its high-order derivative maps","volume":"42","author":"Xu","journal-title":"Abdom Radiol (NY)"},{"issue":"5","key":"R64","doi-asserted-by":"publisher","first-page":"1489","DOI":"10.1002\/jmri.26327","article-title":"Quantitative identification of nonmuscle\u2010invasive and muscle\u2010invasive bladder carcinomas: a multiparametric MRI radiomics analysis","volume":"49","author":"Xu","journal-title":"J Magn Reson Imaging"},{"issue":"12","key":"R65","doi-asserted-by":"publisher","first-page":"2685","DOI":"10.1002\/jum.15659","article-title":"Radiomics analysis based on ultrasound images to distinguish the tumor stage and pathological grade of bladder cancer","volume":"40","author":"Gao","journal-title":"J Ultrasound Med"},{"issue":"11","key":"R66","doi-asserted-by":"publisher","first-page":"6182","DOI":"10.1007\/s00330-019-06222-8","article-title":"Radiomics analysis of multiparametric MRI for the preoperative evaluation of pathological grade in bladder cancer tumors","volume":"29","author":"Wang","journal-title":"Eur Radiol"},{"issue":"5","key":"R67","doi-asserted-by":"publisher","first-page":"1281","DOI":"10.1002\/jmri.25669","article-title":"Radiomics assessment of bladder cancer grade using texture features from diffusion-weighted imaging","volume":"46","author":"Zhang","journal-title":"J Magn Reson Imaging"},{"issue":"1","key":"R68","doi-asserted-by":"publisher","first-page":"145","DOI":"10.1016\/j.euf.2022.08.015","article-title":"Radiomics signature using manual versus automated segmentation for lymph node staging of bladder cancer","volume":"9","author":"Gresser","journal-title":"Eur Urol Focus"},{"issue":"3","key":"R69","doi-asserted-by":"publisher","first-page":"331","DOI":"10.1016\/j.adro.2018.04.011","article-title":"Radiomics-guided therapy for bladder cancer: using an optimal biomarker approach to determine extent of bladder cancer invasion from t2-weighted magnetic resonance images","volume":"3","author":"Tong","journal-title":"Adv Radiat Oncol"},{"key":"R70","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2024.1337186","article-title":"SMMF: a self-attention-based multi-parametric MRI feature fusion framework for the diagnosis of bladder cancer grading","volume":"14","author":"Tao","journal-title":"Front Oncol"},{"issue":"1","key":"R71","doi-asserted-by":"publisher","first-page":"64","DOI":"10.1016\/j.acra.2022.04.014","article-title":"The additional value of tri-parametric MRI in identifying muscle-invasive status in bladder cancer","volume":"30","author":"Liu","journal-title":"Acad Radiol"},{"key":"R72","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2022.106635","article-title":"The effect of CT high-resolution imaging diagnosis based on deep residual network on the pathology of bladder cancer classification and staging","volume":"215","author":"Liu","journal-title":"Comput Methods Programs Biomed"},{"issue":"1122","key":"R73","doi-asserted-by":"publisher","DOI":"10.1259\/bjr.20201114","article-title":"Utility of first order MRI-Texture analysis parameters in the prediction of histologic grade and muscle invasion in urinary bladder cancer: a preliminary study","volume":"94","author":"Razik","journal-title":"Br J Radiol"},{"key":"R74","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2024.1427122","article-title":"Contrast-enhanced computed tomography-based radiomics nomogram for predicting HER2 status in urothelial bladder carcinoma","volume":"14","author":"Peng","journal-title":"Front Oncol"},{"issue":"9","key":"R75","doi-asserted-by":"publisher","first-page":"5965","DOI":"10.1002\/mp.17288","article-title":"Machine learning\u2010based CT radiomics enhances bladder cancer staging predictions: a comparative study of clinical, radiomics, and combined models","volume":"51","author":"Xiong","journal-title":"Med Phys"},{"key":"R76","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2024.1276526","article-title":"Prediction of Ki-67 expression in bladder cancer based on CT radiomics nomogram","volume":"14","author":"Feng","journal-title":"Front Oncol"},{"issue":"1","key":"R77","doi-asserted-by":"publisher","DOI":"10.1186\/s13244-024-01840-3","article-title":"A radiomics-based interpretable machine learning model to predict the HER2 status in bladder cancer: a multicenter study","volume":"15","author":"Wei","journal-title":"Insights Imaging"},{"issue":"1","key":"R78","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-024-82909-3","article-title":"Deep learning on T2WI to predict the muscle-invasive bladder cancer: a multi-center clinical study","volume":"15","author":"Cai","journal-title":"Sci Rep"},{"issue":"12","key":"R79","doi-asserted-by":"publisher","first-page":"7204","DOI":"10.1016\/j.acra.2025.08.018","article-title":"Multi-DECT image-based interpretable model incorporating habitat radiomics and vision transformer deep learning for preoperative prediction of muscle invasion in bladder cancer","volume":"32","author":"Du","journal-title":"Acad Radiol"},{"issue":"8","key":"R80","doi-asserted-by":"publisher","DOI":"10.1002\/mp.18021","article-title":"Habitat-based radiomic model for predicting muscle invasion in bladder cancer: a multi-center study using enhanced-CT and machine learning","volume":"52","author":"Du","journal-title":"Med Phys"},{"key":"R81","doi-asserted-by":"publisher","DOI":"10.2174\/0115734056377754250304040058","article-title":"A machine learning model based on multi-phase contrast-enhanced CT for the preoperative prediction of the muscle-invasive status of bladder cancer","volume":"21","author":"He","journal-title":"Curr Med Imaging"},{"issue":"1","key":"R82","doi-asserted-by":"publisher","first-page":"287","DOI":"10.1016\/j.acra.2024.08.010","article-title":"Multi-DECT image-based intratumoral and peritumoral radiomics for preoperative prediction of muscle invasion in bladder cancer","volume":"32","author":"Hu","journal-title":"Acad Radiol"},{"issue":"6","key":"R83","doi-asserted-by":"publisher","first-page":"1568","DOI":"10.1177\/1088467X241313324","article-title":"Multi-path neural network based on mp-MRI for predicting muscle-invasive bladder cancer","volume":"29","author":"Yu","journal-title":"Intell Data Anal"},{"key":"R84","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2025.1661979","article-title":"Development of a radiomics and clinical feature-based nomogram for preoperative prediction of pathological grade in bladder cancer","volume":"15","author":"Zhou","journal-title":"Front Oncol"},{"issue":"2","key":"R85","doi-asserted-by":"publisher","first-page":"371","DOI":"10.1148\/rg.322115125","article-title":"Urinary bladder cancer: role of MR imaging","volume":"32","author":"Verma","journal-title":"Radiographics"},{"issue":"9","key":"R86","doi-asserted-by":"publisher","first-page":"6526","DOI":"10.1007\/s00330-022-08712-8","article-title":"A deep learning masked segmentation alternative to manual segmentation in biparametric MRI prostate cancer radiomics","volume":"32","author":"Bleker","journal-title":"Eur Radiol"},{"issue":"4","key":"R87","doi-asserted-by":"publisher","first-page":"882","DOI":"10.1002\/jmri.26294","article-title":"Update on multiparametric MRI of urinary bladder cancer","volume":"48","author":"van der Pol","journal-title":"J Magn Reson Imaging"},{"issue":"3","key":"R88","doi-asserted-by":"publisher","first-page":"649","DOI":"10.1002\/jmri.27090","article-title":"MRI of bladder cancer: local and nodal staging","volume":"52","author":"Caglic","journal-title":"J Magn Reson Imaging"},{"issue":"12","key":"R89","doi-asserted-by":"publisher","first-page":"3827","DOI":"10.1007\/s00261-019-02276-w","article-title":"Bladder cancer and its mimics: a sonographic pictorial review with CT\/MR and histologic correlation","volume":"44","author":"Wentland","journal-title":"Abdom Radiol (NY)"},{"issue":"22","key":"R90","doi-asserted-by":"publisher","DOI":"10.3390\/cancers15225468","article-title":"Multiparametric MRI in era of artificial intelligence for bladder cancer therapies","volume":"15","author":"Akin","journal-title":"Cancers (Basel)"},{"key":"R91","doi-asserted-by":"publisher","DOI":"10.1016\/j.eclinm.2025.103640","article-title":"A non-invasive MRI-based multimodal fusion deep learning model (MF-DLM) for predicting overall survival in bladder cancer: a multicentre retrospective study","volume":"90","author":"Cai","journal-title":"EClinicalMedicine"},{"issue":"5","key":"R92","doi-asserted-by":"publisher","first-page":"2922","DOI":"10.1097\/JS9.0000000000001194","article-title":"A CT-based deep learning model predicts overall survival in patients with muscle invasive bladder cancer after radical cystectomy: a multicenter retrospective cohort study","volume":"110","author":"Wei","journal-title":"Int J Surg"},{"key":"R93","doi-asserted-by":"publisher","DOI":"10.1016\/j.eclinm.2025.103125","article-title":"Deep learning-based model for prediction of early recurrence and therapy response on whole slide images in non-muscle-invasive bladder cancer: a retrospective, multicentre study","volume":"81","author":"Jiang","journal-title":"EClinicalMedicine"},{"issue":"1","key":"R94","doi-asserted-by":"publisher","first-page":"165","DOI":"10.1016\/j.euf.2020.12.008","article-title":"Deep learning-based recurrence prediction in patients with non-muscle-invasive bladder cancer","volume":"8","author":"Lucas","journal-title":"Eur Urol Focus"},{"issue":"1","key":"R95","doi-asserted-by":"publisher","DOI":"10.1186\/s12916-019-1466-7","article-title":"Calibration: the Achilles heel of predictive analytics","volume":"17","author":"Van Calster","journal-title":"BMC Med"},{"issue":"5","key":"R96","doi-asserted-by":"publisher","first-page":"440","DOI":"10.1016\/j.annemergmed.2022.05.011","article-title":"Machine learning in the prediction of trauma outcomes: a systematic review","volume":"80","author":"Zhang","journal-title":"Ann Emerg Med"},{"issue":"9","key":"R97","doi-asserted-by":"publisher","first-page":"15664","DOI":"10.1109\/TNNLS.2025.3565159","article-title":"A survey on confidence calibration of deep learning-based classification models under class imbalance data","volume":"36","author":"Dong","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"issue":"1","key":"R98","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-023-00753-7","article-title":"Making machine learning matter to clinicians: model actionability in medical decision-making","volume":"6","author":"Ehrmann","journal-title":"NPJ Digit Med"}],"container-title":["Journal of Medical Internet Research"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.jmir.org\/2026\/1\/e81084","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,31]],"date-time":"2026-07-31T22:26:35Z","timestamp":1785536795000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.jmir.org\/2026\/1\/e81084"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6,12]]},"references-count":98,"URL":"https:\/\/doi.org\/10.2196\/81084","relation":{},"ISSN":["1438-8871"],"issn-type":[{"value":"1438-8871","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,6,12]]},"article-number":"v28i3e81084"}}