{"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":1791578391506,"version":"4.3.4"},"reference-count":106,"publisher":"Elsevier BV","issue":"12","license":[{"start":{"date-parts":[[2025,12,1]],"date-time":"2025-12-01T00:00:00Z","timestamp":1764547200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2025,12,1]],"date-time":"2025-12-01T00:00:00Z","timestamp":1764547200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"}],"funder":[{"DOI":"10.13039\/501100003996","name":"Kunming Medical University","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100003996","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100007846","name":"Yunnan Provincial Department of Education","doi-asserted-by":"publisher","award":["2024Y231"],"award-info":[{"award-number":["2024Y231"]}],"id":[{"id":"10.13039\/501100007846","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["academicradiology.org","clinicalkey.com","clinicalkey.com.au","clinicalkey.es","clinicalkey.fr","clinicalkey.jp","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Academic Radiology"],"published-print":{"date-parts":[[2025,12]]},"DOI":"10.1016\/j.acra.2025.08.035","type":"journal-article","created":{"date-parts":[[2025,9,5]],"date-time":"2025-09-05T21:11:09Z","timestamp":1757106669000},"page":"7215-7243","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":9,"title":["AI-driven and Traditional Radiomic Model for Predicting Muscle Invasion in Bladder Cancer via Multi-parametric Imaging: A Systematic Review and Meta-analysis"],"prefix":"10.1016","volume":"32","author":[{"given":"Zhengyan","family":"Wang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongjin","family":"Shi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qun","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yu","family":"Huang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mingyuan","family":"Feng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lin","family":"Yu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Baonan","family":"Dong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianxiong","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xin","family":"Deng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shi","family":"Fu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guifu","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0360-1402","authenticated-orcid":false,"given":"Haifeng","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"1","key":"10.1016\/j.acra.2025.08.035_bib1","first-page":"12","article-title":"Cancer statistics, 2024","volume":"74","author":"Siegel","year":"2024","journal-title":"CA: a cancer journal for clinicians"},{"issue":"1","key":"10.1016\/j.acra.2025.08.035_bib2","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1038\/s41572-023-00468-9","article-title":"Bladder cancer","volume":"9","author":"Dyrskj\u00f8t","year":"2023","journal-title":"Nat Rev Dis Primers"},{"issue":"8","key":"10.1016\/j.acra.2025.08.035_bib3","doi-asserted-by":"crossref","first-page":"866","DOI":"10.6004\/jnccn.2022.0041","article-title":"NCCN Guidelines\u00ae Insights: Bladder Cancer, Version 2.2022","volume":"20","author":"Flaig","year":"2022","journal-title":"J Natl Comprehens Cancer Netw: JNCCN"},{"key":"10.1016\/j.acra.2025.08.035_bib4","article-title":"Advances in diagnosis and treatment of bladder cancer","volume":"384","author":"Lopez-Beltran","year":"2024","journal-title":"BMJ (Clinical research ed)"},{"issue":"5","key":"10.1016\/j.acra.2025.08.035_bib5","doi-asserted-by":"crossref","DOI":"10.3390\/jpm13050756","article-title":"Precision medicine in bladder cancer: present challenges and future directions","volume":"13","author":"Mohanty","year":"2023","journal-title":"J Personal Med"},{"issue":"5","key":"10.1016\/j.acra.2025.08.035_bib6","doi-asserted-by":"crossref","first-page":"523","DOI":"10.1016\/S0302-2838(02)00068-4","article-title":"Variability in the recurrence rate at first follow-up cystoscopy after TUR in stage Ta T1 transitional cell carcinoma of the bladder: a combined analysis of seven EORTC studies","volume":"41","author":"Brausi","year":"2002","journal-title":"Eur Urol"},{"issue":"1","key":"10.1016\/j.acra.2025.08.035_bib7","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1016\/j.eururo.2009.08.024","article-title":"An updated critical analysis of the treatment strategy for newly diagnosed high-grade T1 (previously T1G3) bladder cancer","volume":"57","author":"Kulkarni","year":"2010","journal-title":"European Urol"},{"issue":"3","key":"10.1016\/j.acra.2025.08.035_bib8","doi-asserted-by":"crossref","first-page":"294","DOI":"10.1016\/j.eururo.2018.04.029","article-title":"Multiparametric magnetic resonance imaging for bladder cancer: development of VI-RADS (vesical imaging-reporting and data system)","volume":"74","author":"Panebianco","year":"2018","journal-title":"Eur Urol"},{"issue":"7","key":"10.1016\/j.acra.2025.08.035_bib9","doi-asserted-by":"crossref","first-page":"1166","DOI":"10.1038\/s41416-023-02383-y","article-title":"A clinically practical model for the preoperative prediction of lymph node metastasis in bladder cancer: a multicohort study","volume":"129","author":"Lu","year":"2023","journal-title":"Br J Cancer"},{"issue":"12","key":"10.1016\/j.acra.2025.08.035_bib10","doi-asserted-by":"crossref","first-page":"6278","DOI":"10.1172\/JCI139597","article-title":"Urine DNA methylation assay enables early detection and recurrence monitoring for bladder cancer","volume":"130","author":"Chen","year":"2020","journal-title":"J Clin Investig"},{"issue":"6","key":"10.1016\/j.acra.2025.08.035_bib11","doi-asserted-by":"crossref","first-page":"894","DOI":"10.1111\/bju.12245","article-title":"Incidence and predictors of understaging in patients with clinical T1 urothelial carcinoma undergoing radical cystectomy","volume":"113","author":"Ark","year":"2014","journal-title":"BJU Int"},{"issue":"10","key":"10.1016\/j.acra.2025.08.035_bib12","doi-asserted-by":"crossref","DOI":"10.3390\/cancers12102994","article-title":"Systematic review and meta-analysis of vesical imaging-reporting and data system (VI-RADS) inter-observer reliability: an added value for muscle invasive bladder cancer detection","volume":"12","author":"Del Giudice","year":"2020","journal-title":"Cancers"},{"issue":"3","key":"10.1016\/j.acra.2025.08.035_bib13","doi-asserted-by":"crossref","first-page":"306","DOI":"10.1016\/j.euo.2020.02.007","article-title":"Diagnostic performance of vesical imaging reporting and data system for the prediction of muscle-invasive bladder cancer: a systematic review and meta-analysis","volume":"3","author":"Woo","year":"2020","journal-title":"Eur Urol Oncol"},{"issue":"3","key":"10.1016\/j.acra.2025.08.035_bib14","doi-asserted-by":"crossref","DOI":"10.1148\/radiol.233459","article-title":"Multiparametric MRI for bladder cancer: a practical approach to the clinical application of VI-RADS","volume":"314","author":"Pecoraro","year":"2025","journal-title":"Radiology"},{"issue":"4","key":"10.1016\/j.acra.2025.08.035_bib15","doi-asserted-by":"crossref","first-page":"441","DOI":"10.1016\/j.ejca.2011.11.036","article-title":"Radiomics: extracting more information from medical images using advanced feature analysis","volume":"48","author":"Lambin","year":"2012","journal-title":"Eur J Cancer (Oxford, England: 1990)"},{"key":"10.1016\/j.acra.2025.08.035_bib16","article-title":"Multi-DECT image-based intratumoral and peritumoral radiomics for preoperative prediction of muscle invasion in bladder cancer","author":"Hu","year":"2024","journal-title":"Acad Radiol"},{"issue":"16","key":"10.1016\/j.acra.2025.08.035_bib17","doi-asserted-by":"crossref","DOI":"10.1016\/j.heliyon.2024.e36144","article-title":"Development of deep learning model for diagnosing muscle-invasive bladder cancer on MRI with vision transformer","volume":"10","author":"Kurata","year":"2024","journal-title":"Heliyon"},{"issue":"12","key":"10.1016\/j.acra.2025.08.035_bib18","doi-asserted-by":"crossref","first-page":"749","DOI":"10.1038\/nrclinonc.2017.141","article-title":"Radiomics: the bridge between medical imaging and personalized medicine","volume":"14","author":"Lambin","year":"2017","journal-title":"Nat Rev Clin Oncol"},{"issue":"4","key":"10.1016\/j.acra.2025.08.035_bib19","doi-asserted-by":"crossref","first-page":"2699","DOI":"10.1007\/s00330-022-09272-7","article-title":"Predicting muscle invasion in bladder cancer by deep learning analysis of MRI: comparison with vesical imaging\u2013reporting and data system","volume":"33","author":"Li","year":"2023","journal-title":"Eur Radiol"},{"issue":"1","key":"10.1016\/j.acra.2025.08.035_bib20","doi-asserted-by":"crossref","first-page":"148","DOI":"10.1186\/s12880-024-01318-0","article-title":"Compressed sensing 3D T2WI radiomics model: improving diagnostic performance in muscle invasion of bladder cancer","volume":"24","author":"Li","year":"2024","journal-title":"BMC Med Imaging"},{"key":"10.1016\/j.acra.2025.08.035_bib21","article-title":"Significance of normalized apparent diffusion coefficient in the vesical imaging-reporting and data system for diagnosing muscle-invasive bladder cancer","author":"Liu","year":"2023","journal-title":"J Magn Reson Imaging"},{"issue":"1","key":"10.1016\/j.acra.2025.08.035_bib22","doi-asserted-by":"crossref","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","year":"2023","journal-title":"Acad Radiol"},{"key":"10.1016\/j.acra.2025.08.035_bib23","first-page":"m2632","article-title":"Preferred reporting items for systematic review and meta-analysis of diagnostic test accuracy studies (PRISMA-DTA): explanation, elaboration, and checklist","volume":"370","author":"Salameh","year":"2020","journal-title":"BMJ (Clinical research ed)"},{"key":"10.1016\/j.acra.2025.08.035_bib24","doi-asserted-by":"crossref","first-page":"j4008","DOI":"10.1136\/bmj.j4008","article-title":"AMSTAR 2: a critical appraisal tool for systematic reviews that include randomised or non-randomised studies of healthcare interventions, or both","volume":"358","author":"Shea","year":"2017","journal-title":"BMJ (Clinical research ed)"},{"issue":"8","key":"10.1016\/j.acra.2025.08.035_bib25","doi-asserted-by":"crossref","first-page":"529","DOI":"10.7326\/0003-4819-155-8-201110180-00009","article-title":"QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies","volume":"155","author":"Whiting","year":"2011","journal-title":"Ann Intern Med"},{"issue":"14","key":"10.1016\/j.acra.2025.08.035_bib26","doi-asserted-by":"crossref","first-page":"1293","DOI":"10.1002\/sim.4780121403","article-title":"Combining independent studies of a diagnostic test into a summary ROC curve: data-analytic approaches and some additional considerations","volume":"12","author":"Moses","year":"1993","journal-title":"Stat Med"},{"issue":"6","key":"10.1016\/j.acra.2025.08.035_bib27","doi-asserted-by":"crossref","first-page":"3333","DOI":"10.1016\/j.acra.2025.01.029","article-title":"Diagnostic values of magnetic resonance imaging and computed tomography for predicting macrotrabecular-massive hepatocellular carcinoma subtype: a meta-analysis","volume":"32","author":"Chai","year":"2025","journal-title":"Acad Radiol"},{"issue":"6","key":"10.1016\/j.acra.2025.08.035_bib28","doi-asserted-by":"crossref","first-page":"3429","DOI":"10.1016\/j.acra.2024.12.006","article-title":"MRI-based radiomics for predicting prostate cancer grade groups: a systematic review and meta-analysis of diagnostic test accuracy studies","volume":"32","author":"Lomer","year":"2025","journal-title":"Acad Radiol"},{"issue":"1","key":"10.1016\/j.acra.2025.08.035_bib29","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1007\/s00223-025-01377-7","article-title":"Accuracy of low-dose chest ct-based artificial intelligence models in osteoporosis detection: a systematic review and meta-analysis","volume":"116","author":"Ya'nan","year":"2025","journal-title":"Calcif Tissue Int"},{"issue":"6","key":"10.1016\/j.acra.2025.08.035_bib30","doi-asserted-by":"crossref","first-page":"2212","DOI":"10.1007\/s00259-025-07069-6","article-title":"PET radiomics for histologic subtype classification of non-small cell lung cancer: a systematic review and meta-analysis","volume":"52","author":"Zhang","year":"2025","journal-title":"Eur J Nucl Med Mol Imaging"},{"issue":"5","key":"10.1016\/j.acra.2025.08.035_bib31","doi-asserted-by":"crossref","first-page":"2863","DOI":"10.1016\/j.acra.2024.11.065","article-title":"The quality and accuracy of radiomics model in diagnosing osteoporosis: a systematic review and meta-analysis","volume":"32","author":"Chen","year":"2025","journal-title":"Acad Radiol"},{"issue":"6","key":"10.1016\/j.acra.2025.08.035_bib32","doi-asserted-by":"crossref","first-page":"3795","DOI":"10.1097\/JS9.0000000000001239","article-title":"Application of radiomics for preoperative prediction of lymph node metastasis in colorectal cancer: a systematic review and meta-analysis","volume":"110","author":"Abbaspour","year":"2024","journal-title":"Int J Surg (London, England)"},{"issue":"3","key":"10.1016\/j.acra.2025.08.035_bib33","doi-asserted-by":"crossref","first-page":"544","DOI":"10.1002\/jrsm.1634","article-title":"Comparison and validation of metadta for meta-analysis of diagnostic test accuracy studies","volume":"14","author":"Nyaga","year":"2023","journal-title":"Research Synth Methods"},{"issue":"4","key":"10.1016\/j.acra.2025.08.035_bib34","doi-asserted-by":"crossref","first-page":"599","DOI":"10.1016\/j.gie.2021.12.021","article-title":"Diagnostic accuracy of convolutional neural network-based endoscopic image analysis in diagnosing gastric cancer and predicting its invasion depth: a systematic review and meta-analysis","volume":"95","author":"Xie","year":"2022","journal-title":"Gastrointest Endosc"},{"key":"10.1016\/j.acra.2025.08.035_bib35","doi-asserted-by":"crossref","first-page":"d4002","DOI":"10.1136\/bmj.d4002","article-title":"Recommendations for examining and interpreting funnel plot asymmetry in meta-analyses of randomised controlled trials","volume":"343","author":"Sterne","year":"2011","journal-title":"BMJ (Clinical research ed)"},{"key":"10.1016\/j.acra.2025.08.035_bib36","doi-asserted-by":"crossref","unstructured":"Benfante V., Salvaggio G., Ali M., et al. Grading and Staging of Bladder Tumors Using Radiomics Analysis in Magnetic Resonance Imaging. 2023:93\u2013103.","DOI":"10.1007\/978-3-031-51026-7_9"},{"issue":"4","key":"10.1016\/j.acra.2025.08.035_bib37","doi-asserted-by":"crossref","first-page":"1179","DOI":"10.1002\/jmri.28924","article-title":"A nomogram of MRI features to assess muscle invasion in VI-RADS 2 tumors with stalk","volume":"59","author":"Cai","year":"2024","journal-title":"J Magn Reson Imaging"},{"key":"10.1016\/j.acra.2025.08.035_bib38","article-title":"CT-based deep learning radiomics signature for the preoperative prediction of the muscle-invasive status of bladder cancer","volume":"12","author":"Chen","year":"2022","journal-title":"Front Oncol"},{"issue":"6","key":"10.1016\/j.acra.2025.08.035_bib39","doi-asserted-by":"crossref","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' assessment","volume":"77","author":"Cui","year":"2022","journal-title":"Clin Radiol"},{"issue":"1","key":"10.1016\/j.acra.2025.08.035_bib40","doi-asserted-by":"crossref","DOI":"10.1186\/s43055-023-01181-z","article-title":"A proposed inclusion of magnetic resonance imaging features to the VI RADS to enhance its accuracy in predicting muscle invasion","volume":"55","author":"Elshewy","year":"2024","journal-title":"Egypt J Radiol Nucl Med"},{"issue":"1","key":"10.1016\/j.acra.2025.08.035_bib41","doi-asserted-by":"crossref","first-page":"138","DOI":"10.1186\/s13244-024-01701-z","article-title":"Non-Gaussian diffusion metrics with whole-tumor histogram analysis for bladder cancer diagnosis: muscle invasion and histological grade","volume":"15","author":"Fan","year":"2024","journal-title":"Insights Imaging"},{"issue":"11","key":"10.1016\/j.acra.2025.08.035_bib42","doi-asserted-by":"crossref","first-page":"5814","DOI":"10.1002\/mp.12510","article-title":"Urinary bladder cancer staging in CT urography using machine learning","volume":"44","author":"Garapati","year":"2017","journal-title":"Med Phys"},{"key":"10.1016\/j.acra.2025.08.035_bib43","doi-asserted-by":"crossref","DOI":"10.1007\/s00261-024-04459-6","article-title":"Impact of virtual monochromatic images of different low-energy levels in dual-energy CT on radiomics models for predicting muscle invasion in bladder cancer","author":"Hu","year":"2024","journal-title":"Abdom Radiol"},{"key":"10.1016\/j.acra.2025.08.035_bib44","doi-asserted-by":"crossref","DOI":"10.1016\/j.canlet.2025.217438","article-title":"A foundation model with weak experiential guidance in detecting muscle invasive bladder cancer on MRI","volume":"611","author":"Gong","year":"2025","journal-title":"Cancer Lett"},{"key":"10.1016\/j.acra.2025.08.035_bib45","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","year":"2025","journal-title":"Curr Med Imaging"},{"issue":"1","key":"10.1016\/j.acra.2025.08.035_bib46","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1186\/s43055-025-01432-1","article-title":"BOLD and DW- MRI metrics in predicting bladder urothelial carcinoma histologic grade and muscle invasion: a prospective study","volume":"56","author":"Ezz Eldin","year":"2025","journal-title":"Egypt J Radiol Nucl Med"},{"issue":"1","key":"10.1016\/j.acra.2025.08.035_bib47","doi-asserted-by":"crossref","first-page":"9942","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","year":"2025","journal-title":"Sci Rep"},{"issue":"10","key":"10.1016\/j.acra.2025.08.035_bib48","doi-asserted-by":"crossref","first-page":"4076","DOI":"10.1016\/j.acra.2024.03.011","article-title":"Differentiation of muscular invasion in bladder cancer: additional value of synthetic magnetic resonance imaging","volume":"31","author":"Zhang","year":"2024","journal-title":"Acad Radiol"},{"issue":"11","key":"10.1016\/j.acra.2025.08.035_bib49","doi-asserted-by":"crossref","first-page":"3883","DOI":"10.1007\/s00261-024-04459-6","article-title":"Impact of virtual monochromatic images of different low-energy levels in dual-energy CT on radiomics models for predicting muscle invasion in bladder cancer","volume":"49","author":"Hu","year":"2024","journal-title":"Abdom Radiol (New York)"},{"issue":"1","key":"10.1016\/j.acra.2025.08.035_bib50","doi-asserted-by":"crossref","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","year":"2025","journal-title":"Acad Radiol"},{"key":"10.1016\/j.acra.2025.08.035_bib51","unstructured":"Yu J., Cai L., Chen C., et al. Multi-path neural network based on mp-MRI for predicting muscle-invasive bladder cancer. 0(0):1088467\u00d7241313324. \u3008doi:10.1177\/1088467\u00d7241313324\u3009."},{"issue":"1","key":"10.1016\/j.acra.2025.08.035_bib52","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1515\/bmt-2024-0333","article-title":"Prediction of muscular-invasive bladder cancer using multi-view fusion self-distillation model based on 3D T2-Weighted images","volume":"70","author":"Zou","year":"2025","journal-title":"Biomed Tech Biomed Eng"},{"issue":"12","key":"10.1016\/j.acra.2025.08.035_bib53","doi-asserted-by":"crossref","first-page":"5034","DOI":"10.1016\/j.acra.2024.07.002","article-title":"Prospective comparison of 2D and 3D T2-weighted imaging in multiparametric MRI for assessing muscle invasion accuracy using VI-RADS in bladder cancer","volume":"31","author":"Zhang","year":"2024","journal-title":"Acad Radiol"},{"issue":"7","key":"10.1016\/j.acra.2025.08.035_bib54","doi-asserted-by":"crossref","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","year":"2017","journal-title":"Abdom Radiol"},{"issue":"3","key":"10.1016\/j.acra.2025.08.035_bib55","doi-asserted-by":"crossref","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","year":"2018","journal-title":"Adv Radiat Oncol"},{"issue":"5","key":"10.1016\/j.acra.2025.08.035_bib56","doi-asserted-by":"crossref","first-page":"1489","DOI":"10.1002\/jmri.26327","article-title":"Quantitative identification of nonmuscle-invasive and muscle-invasive bladder carcinomas: a multiparametric MRI radiomics analysis","volume":"49","author":"Xu","year":"2019","journal-title":"J Magn Reson Imaging"},{"issue":"24","key":"10.1016\/j.acra.2025.08.035_bib57","doi-asserted-by":"crossref","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","year":"2019","journal-title":"Cancer"},{"issue":"9","key":"10.1016\/j.acra.2025.08.035_bib58","doi-asserted-by":"crossref","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","year":"2020","journal-title":"Eur Radiol"},{"issue":"3","key":"10.1016\/j.acra.2025.08.035_bib59","doi-asserted-by":"crossref","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","year":"2020","journal-title":"Eur Radiol"},{"key":"10.1016\/j.acra.2025.08.035_bib60","article-title":"Deep learning on enhanced CT images can predict the muscular invasiveness of bladder cancer","volume":"11","author":"Zhang","year":"2021","journal-title":"Front Oncol"},{"key":"10.1016\/j.acra.2025.08.035_bib61","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","year":"2021","journal-title":"Front Oncol"},{"issue":"5","key":"10.1016\/j.acra.2025.08.035_bib62","doi-asserted-by":"crossref","first-page":"3260","DOI":"10.1007\/s00330-021-08426-3","article-title":"CT-based radiomics to predict muscle invasion in bladder cancer","volume":"32","author":"Zhang","year":"2022","journal-title":"Eur Radiol"},{"key":"10.1016\/j.acra.2025.08.035_bib63","doi-asserted-by":"crossref","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","year":"2022","journal-title":"Comput Biol Med"},{"issue":"9","key":"10.1016\/j.acra.2025.08.035_bib64","article-title":"Can only the shape feature in radiomics help machine learning show that bladder cancer has invaded muscles?","volume":"15","author":"\u00d6zdemir","year":"2023","journal-title":"Cureus"},{"issue":"1","key":"10.1016\/j.acra.2025.08.035_bib65","doi-asserted-by":"crossref","DOI":"10.1186\/s43055-023-01044-7","article-title":"Preoperative CT-based radiomics for diagnosing muscle invasion of bladder cancer","volume":"54","author":"Ren","year":"2023","journal-title":"Egypt J Radiol Nucl Med"},{"issue":"12","key":"10.1016\/j.acra.2025.08.035_bib66","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","year":"2023","journal-title":"Bioengineering (Basel, Switzerland)"},{"issue":"1","key":"10.1016\/j.acra.2025.08.035_bib67","doi-asserted-by":"crossref","first-page":"258","DOI":"10.1002\/jmri.28498","article-title":"Multi-sequence and multi-regional MRI-based radiomics nomogram for the preoperative assessment of muscle invasion in bladder cancer","volume":"58","author":"Zhang","year":"2023","journal-title":"J Magn Reson Imaging"},{"issue":"4","key":"10.1016\/j.acra.2025.08.035_bib68","first-page":"394","article-title":"Integrating the continuous-time random-walk diffusion model and the vesical imaging-reporting and data system to predict muscle invasion of bladder cancer","volume":"58","author":"Wang","year":"2024","journal-title":"Chin J Radiol"},{"issue":"2","key":"10.1016\/j.acra.2025.08.035_bib69","doi-asserted-by":"crossref","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","year":"2024","journal-title":"Heliyon"},{"key":"10.1016\/j.acra.2025.08.035_bib70","doi-asserted-by":"crossref","DOI":"10.1002\/mp.17288","article-title":"Machine learning-based CT radiomics enhances bladder cancer staging predictions: a comparative study of clinical, radiomics, and combined models","author":"Xiong","year":"2024","journal-title":"Med Phys"},{"issue":"5","key":"10.1016\/j.acra.2025.08.035_bib71","doi-asserted-by":"crossref","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","year":"2024","journal-title":"Phys Med Biol"},{"issue":"2","key":"10.1016\/j.acra.2025.08.035_bib72","doi-asserted-by":"crossref","first-page":"448","DOI":"10.1002\/jmri.29103","article-title":"Development and validation of an MRI-based nomogram for preoperative detection of muscle invasion in VI-RADS 3","volume":"60","author":"Yu","year":"2024","journal-title":"J Magn Reson Imaging: JMRI"},{"issue":"1","key":"10.1016\/j.acra.2025.08.035_bib73","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1016\/j.eururo.2023.08.016","article-title":"European Association of Urology Guidelines on Muscle-invasive and Metastatic Bladder Cancer: Summary of the 2023 Guidelines","volume":"85","author":"Alfred Witjes","year":"2024","journal-title":"Eur Urol"},{"issue":"6","key":"10.1016\/j.acra.2025.08.035_bib74","doi-asserted-by":"crossref","first-page":"1989","DOI":"10.1002\/jmri.27770","article-title":"Investigation of synthetic magnetic resonance imaging applied in the evaluation of the tumor grade of bladder cancer","volume":"54","author":"Cai","year":"2021","journal-title":"J Magn Reson Imaging: JMRI"},{"issue":"10","key":"10.1016\/j.acra.2025.08.035_bib75","doi-asserted-by":"crossref","first-page":"2178","DOI":"10.1007\/s00330-011-2174-7","article-title":"Diagnostic performance of diffusion-weighted magnetic resonance imaging in bladder cancer: potential utility of apparent diffusion coefficient values as a biomarker to predict clinical aggressiveness","volume":"21","author":"Kobayashi","year":"2011","journal-title":"Eur Radiol"},{"issue":"1","key":"10.1016\/j.acra.2025.08.035_bib76","doi-asserted-by":"crossref","first-page":"201","DOI":"10.1007\/s00330-006-0281-7","article-title":"Diffusion-weighted MR imaging for urinary bladder carcinoma: initial results","volume":"17","author":"Matsuki","year":"2007","journal-title":"Eur Radiol"},{"issue":"6","key":"10.1016\/j.acra.2025.08.035_bib77","doi-asserted-by":"crossref","first-page":"1254","DOI":"10.2214\/AJR.12.10348","article-title":"Utility of quantitative MRI metrics for assessment of stage and grade of urothelial carcinoma of the bladder: preliminary results","volume":"201","author":"Rosenkrantz","year":"2013","journal-title":"AJR Am J Roentgenol"},{"issue":"8","key":"10.1016\/j.acra.2025.08.035_bib78","doi-asserted-by":"crossref","first-page":"1027","DOI":"10.1016\/j.acra.2014.03.004","article-title":"Histogram analysis of apparent diffusion coefficient at 3.0 T in urinary bladder lesions: correlation with pathologic findings","volume":"21","author":"Suo","year":"2014","journal-title":"Academic radiology"},{"issue":"4","key":"10.1016\/j.acra.2025.08.035_bib79","doi-asserted-by":"crossref","first-page":"1054","DOI":"10.1002\/jmri.25839","article-title":"Intravoxel incoherent motion diffusion-weighted imaging in assessing bladder cancer invasiveness and cell proliferation","volume":"47","author":"Wang","year":"2018","journal-title":"J Magn Reson Imaging: JMRI"},{"issue":"4","key":"10.1016\/j.acra.2025.08.035_bib80","doi-asserted-by":"crossref","first-page":"E173","DOI":"10.25011\/cim.v38i4.24262","article-title":"Sensitivity and specificity of diffusion-weighted magnetic resonance imaging in diagnosis of bladder cancers","volume":"38","author":"Zhai","year":"2015","journal-title":"Clin Investig Med Medecine clinique et experimentale"},{"issue":"9","key":"10.1016\/j.acra.2025.08.035_bib81","doi-asserted-by":"crossref","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","year":"2020","journal-title":"Eur Radiol"},{"issue":"1","key":"10.1016\/j.acra.2025.08.035_bib82","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1186\/s40644-020-00304-3","article-title":"Evaluation of the value of the VI-RADS scoring system in assessing muscle infiltration by bladder cancer","volume":"20","author":"Wang","year":"2020","journal-title":"Cancer Imaging"},{"issue":"5","key":"10.1016\/j.acra.2025.08.035_bib83","doi-asserted-by":"crossref","first-page":"1489","DOI":"10.1002\/jmri.26327","article-title":"Quantitative identification of nonmuscle-invasive and muscle-invasive bladder carcinomas: a multiparametric MRI radiomics analysis","volume":"49","author":"Xu","year":"2019","journal-title":"J Magn Reson Imaging: JMRI"},{"issue":"12","key":"10.1016\/j.acra.2025.08.035_bib84","doi-asserted-by":"crossref","first-page":"1964","DOI":"10.1016\/j.ultrasmedbio.2024.09.002","article-title":"Ultrasound-based radiomics analysis for assessing risk factors associated with early recurrence following surgical resection of hepatocellular carcinoma","volume":"50","author":"Cao","year":"2024","journal-title":"Ultrasound Med Biol"},{"issue":"12","key":"10.1016\/j.acra.2025.08.035_bib85","doi-asserted-by":"crossref","first-page":"4239","DOI":"10.1007\/s00261-024-04290-z","article-title":"Pre-operative prediction of histopathological growth patterns of colorectal cancer liver metastasis using MRI-based radiomic models","volume":"49","author":"Song","year":"2024","journal-title":"Abdom Radiol (New York)"},{"key":"10.1016\/j.acra.2025.08.035_bib86","doi-asserted-by":"crossref","DOI":"10.1007\/s00261-024-04685-y","article-title":"Radiomics-based automated machine learning for differentiating focal liver lesions on unenhanced computed tomography","author":"Yang","year":"2024","journal-title":"Abdom Radiol (New York)"},{"key":"10.1016\/j.acra.2025.08.035_bib87","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2024.109249","article-title":"Role of [(18)F]-PSMA-1007 PET radiomics for seminal vesicle invasion prediction in primary prostate cancer","volume":"183","author":"Luo","year":"2024","journal-title":"Comput Biol Med"},{"issue":"12","key":"10.1016\/j.acra.2025.08.035_bib88","doi-asserted-by":"crossref","first-page":"7909","DOI":"10.1007\/s00330-024-10818-0","article-title":"Prostate cancer risk assessment and avoidance of prostate biopsies using fully automatic deep learning in prostate MRI: comparison to PI-RADS and integration with clinical data in nomograms","volume":"34","author":"Schrader","year":"2024","journal-title":"Eur Radiol"},{"issue":"4","key":"10.1016\/j.acra.2025.08.035_bib89","doi-asserted-by":"crossref","DOI":"10.1177\/15593258241301525","article-title":"Prediction of neoadjuvant chemoradiotherapy sensitivity in patients with esophageal squamous cell carcinoma using CT-based radiomics combined with clinical features","volume":"22","author":"Li","year":"2024","journal-title":"Dose-response: a publication of International Hormesis Society. Oct-Dec"},{"issue":"1","key":"10.1016\/j.acra.2025.08.035_bib90","doi-asserted-by":"crossref","first-page":"313","DOI":"10.1186\/s12880-024-01473-4","article-title":"Prediction of esophageal fistula in radiotherapy\/chemoradiotherapy for patients with advanced esophageal cancer by a clinical-deep learning radiomics model: Prediction of esophageal fistula in radiotherapy\/chemoradiotherapy patients","volume":"24","author":"Zhang","year":"2024","journal-title":"BMC Med Imaging"},{"issue":"2","key":"10.1016\/j.acra.2025.08.035_bib91","doi-asserted-by":"crossref","first-page":"818","DOI":"10.3390\/curroncol31020060","article-title":"Diagnostic accuracy of contrast-enhanced ultrasound (CEUS) in the detection of muscle-invasive bladder cancer: a systematic review and diagnostic meta-analysis","volume":"31","author":"Tufano","year":"2024","journal-title":"Curr Oncol (Toronto, Ont)"},{"issue":"1","key":"10.1016\/j.acra.2025.08.035_bib92","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1002\/jmri.27361","article-title":"VI-RADS for bladder cancer: current applications and future developments","volume":"55","author":"Panebianco","year":"2022","journal-title":"J Magn Reson Imaging: JMRI"},{"issue":"2","key":"10.1016\/j.acra.2025.08.035_bib93","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1016\/j.urolonc.2021.11.008","article-title":"Diagnostic accuracy of vesical imaging-reporting and data system (VI-RADS) in suspected muscle invasive bladder cancer: A systematic review and diagnostic meta-analysis","volume":"40","author":"Jazayeri","year":"2022","journal-title":"Urol Oncol"},{"issue":"9","key":"10.1016\/j.acra.2025.08.035_bib94","doi-asserted-by":"crossref","first-page":"6480","DOI":"10.1007\/s00330-022-08696-5","article-title":"Biparametric magnetic resonance imaging assessment for detection of muscle-invasive bladder cancer: a systematic review and meta-analysis","volume":"32","author":"Ye","year":"2022","journal-title":"Eur Radiol"},{"issue":"3","key":"10.1016\/j.acra.2025.08.035_bib95","doi-asserted-by":"crossref","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","year":"2022","journal-title":"Eur Urol Focus"},{"key":"10.1016\/j.acra.2025.08.035_bib96","doi-asserted-by":"crossref","DOI":"10.1038\/srep13087","article-title":"Machine learning methods for quantitative radiomic biomarkers","volume":"5","author":"Parmar","year":"2015","journal-title":"Sci Rep"},{"issue":"5","key":"10.1016\/j.acra.2025.08.035_bib97","doi-asserted-by":"crossref","first-page":"e185","DOI":"10.1002\/mp.13678","article-title":"Machine and deep learning methods for radiomics","volume":"47","author":"Avanzo","year":"2020","journal-title":"Med Phys"},{"key":"10.1016\/j.acra.2025.08.035_bib98","doi-asserted-by":"crossref","DOI":"10.1016\/j.compmedimag.2025.102541","article-title":"Automatic semantic segmentation of the osseous structures of the paranasal sinuses","volume":"123","author":"Sun","year":"2025","journal-title":"Comput Med Imaging Graphics: the official journal of the Computerized Medical Imaging Society"},{"key":"10.1016\/j.acra.2025.08.035_bib99","doi-asserted-by":"crossref","DOI":"10.1016\/j.ejro.2024.100582","article-title":"Automated algorithm for medical data structuring, and segmentation using artificial intelligence within secured environment for dataset creation","volume":"13","author":"Nainamalai","year":"2024","journal-title":"Eur J Radiol Open"},{"issue":"4","key":"10.1016\/j.acra.2025.08.035_bib100","doi-asserted-by":"crossref","first-page":"243","DOI":"10.1038\/s41585-023-00830-2","article-title":"Clinical application of bladder MRI and the vesical imaging-reporting and data system","volume":"21","author":"Panebianco","year":"2024","journal-title":"Nat Rev Urol"},{"issue":"5","key":"10.1016\/j.acra.2025.08.035_bib101","doi-asserted-by":"crossref","first-page":"1257","DOI":"10.2214\/AJR.20.23604","article-title":"VI-RADS: multiinstitutional multireader diagnostic accuracy and interobserver agreement study","volume":"216","author":"Ueno","year":"2021","journal-title":"AJR Am J Roentgenol"},{"issue":"10","key":"10.1016\/j.acra.2025.08.035_bib102","doi-asserted-by":"crossref","first-page":"7209","DOI":"10.1007\/s00330-023-09677-y","article-title":"VI-RADS for the diagnosis and management of urinary bladder cancer","volume":"33","author":"Panebianco","year":"2023","journal-title":"Eur Radiol"},{"issue":"3","key":"10.1016\/j.acra.2025.08.035_bib103","doi-asserted-by":"crossref","first-page":"668","DOI":"10.1148\/radiol.2019182506","article-title":"Multiparametric MRI for bladder cancer: validation of VI-RADS for the detection of detrusor muscle invasion","volume":"291","author":"Wang","year":"2019","journal-title":"Radiology"},{"issue":"1","key":"10.1016\/j.acra.2025.08.035_bib104","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1186\/s13244-024-01662-3","article-title":"Enhancing recurrence risk prediction for bladder cancer using multi-sequence MRI radiomics","volume":"15","author":"Yang","year":"2024","journal-title":"Insights Imaging"},{"issue":"3","key":"10.1016\/j.acra.2025.08.035_bib105","doi-asserted-by":"crossref","first-page":"1804","DOI":"10.1007\/s00330-023-10137-w","article-title":"MRI-based automated machine learning model for preoperative identification of variant histology in muscle-invasive bladder carcinoma","volume":"34","author":"Huang","year":"2024","journal-title":"Eur Radiol"},{"issue":"6","key":"10.1016\/j.acra.2025.08.035_bib106","doi-asserted-by":"crossref","first-page":"294.e1","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","year":"2023","journal-title":"Urol Oncol"}],"container-title":["Academic Radiology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1076633225008062?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1076633225008062?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,3,7]],"date-time":"2026-03-07T11:16:30Z","timestamp":1772882190000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1076633225008062"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12]]},"references-count":106,"journal-issue":{"issue":"12","published-print":{"date-parts":[[2025,12]]}},"alternative-id":["S1076633225008062"],"URL":"https:\/\/doi.org\/10.1016\/j.acra.2025.08.035","relation":{},"ISSN":["1076-6332"],"issn-type":[{"value":"1076-6332","type":"print"}],"subject":[],"published":{"date-parts":[[2025,12]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"AI-driven and Traditional Radiomic Model for Predicting Muscle Invasion in Bladder Cancer via Multi-parametric Imaging: A Systematic Review and Meta-analysis","name":"articletitle","label":"Article Title"},{"value":"Academic Radiology","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.acra.2025.08.035","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2025 The Association of Academic Radiology. Published by Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}]}}