{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T01:22:18Z","timestamp":1780536138712,"version":"3.54.1"},"reference-count":61,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2026,1,22]],"date-time":"2026-01-22T00:00:00Z","timestamp":1769040000000},"content-version":"vor","delay-in-days":21,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"name":"NIH\/NCI Cancer Center Support Grant","award":["P30 CA008748"],"award-info":[{"award-number":["P30 CA008748"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,1,23]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Objectives<\/jats:title>\n                    <jats:p>There is a growing need to develop user-friendly, bladder-specific image analysis tools that can produce reliable artificial intelligence (AI)-quantitative imaging biomarkers (QIBs) derived from multiparametric (mp)MRI data for clinical applications. To address it, we developed an AI-powered BLADdEr multiparametric MRI Analysis for Clinical Application (AI-BLADE, current release v1.0) toolbox designed for extracting mpMRI-derived quantitative metrics.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>AI-BLADE is an advanced tool for bladder-specific mpMRI data analysis with 2 core functionalities: (1) Deep Feature Analysis (MRI-DFA toolkit) and (2) Data-Driven Model-Based Analysis (MRI-MBA toolkit). AI-BLADE offers customizable options and serves as a one-stop shop solution for bladder cancer (BCa) clinical applications. The models within DFA and MBA were tested separately on 2 patient cohorts. DFA was used to classify BCa histology subtypes (n\u2009=\u2009104) with T2-weighted images, while MBA was used to interrogate tumour physiology by deriving mpMRI QIBs, including apparent diffusion coefficient (ADC), and volume transfer constant (Ktrans) obtained from 34 BCa patients.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Out of the 17 AI models tested, the VGG19 model with a decision tree classifier and no feature selection for the fully connected layer 7 achieved the highest area under the curve of the receiver operating characteristic of 0.79 in classifying BCa histology subtypes, demonstrating the strongest performance. The mean ADC and Ktrans values were 1.22 \u00d7 10\u22123 (mm2\/s) and 0.27 (min\u22121), respectively, reflecting underlying tumour physiology.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The AI-BLADE (v1.0), a flexible and user-friendly software toolbox for analysing mpMRI data, shows strong potential for application in BCa oncology, offering capabilities that can enhance diagnostic accuracy and support improved patient outcomes.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Advances in knowledge<\/jats:title>\n                    <jats:p>This is the first study to design, develop, and implement a novel bladder-specific AI toolbox for analysing mpMRI data. AI-BLADE enables an advanced image analysis workflow, facilitating AI-QIB-based clinical decision-making for patients with BCa.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/bjrai\/ubag002","type":"journal-article","created":{"date-parts":[[2026,1,22]],"date-time":"2026-01-22T15:53:35Z","timestamp":1769097215000},"source":"Crossref","is-referenced-by-count":2,"title":["AI-BLADE toolbox: AI-powered BLADdEr multiparametric MRI analysis for clinical application"],"prefix":"10.1093","volume":"3","author":[{"given":"Muhammad","family":"Awais","sequence":"first","affiliation":[{"name":"Department of Medical Physics, Memorial Sloan Kettering Cancer Center , New York, NY 10065,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0302-211X","authenticated-orcid":false,"given":"Ramesh","family":"Paudyal","sequence":"additional","affiliation":[{"name":"Department of Medical Physics, Memorial Sloan Kettering Cancer Center , New York, NY 10065,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Oguz","family":"Akin","sequence":"additional","affiliation":[{"name":"Department of Radiology, Memorial Sloan Kettering Cancer Center , New York, NY 10065,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Samuel","family":"Gold","sequence":"additional","affiliation":[{"name":"Department of Surgery, Memorial Sloan Kettering Cancer Center , New York, NY 10065,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Stephanie","family":"Chahwan","sequence":"additional","affiliation":[{"name":"Department of Radiology, Memorial Sloan Kettering Cancer Center , New York, NY 10065,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Josip","family":"Nincevic","sequence":"additional","affiliation":[{"name":"Department of Radiology, Memorial Sloan Kettering Cancer Center , New York, NY 10065,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lisa","family":"Ruby","sequence":"additional","affiliation":[{"name":"Department of Radiology, Memorial Sloan Kettering Cancer Center , New York, NY 10065,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bernard H","family":"Bochner","sequence":"additional","affiliation":[{"name":"Department of Surgery, Memorial Sloan Kettering Cancer Center , New York, NY 10065,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jonathan","family":"Rosenberg","sequence":"additional","affiliation":[{"name":"Department of Medicine, Memorial Sloan Kettering Cancer Center , New York, NY 10065,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"David","family":"Aggen","sequence":"additional","affiliation":[{"name":"Department of Medicine, Memorial Sloan Kettering Cancer Center , New York, NY 10065,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hikmat","family":"Al-Ahmadie","sequence":"additional","affiliation":[{"name":"Department of Pathology, Memorial Sloan Kettering Cancer Center , New York, NY 10065,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alvin C","family":"Goh","sequence":"additional","affiliation":[{"name":"Department of Surgery, Memorial Sloan Kettering Cancer Center , New York, NY 10065,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lawrence H","family":"Schwartz","sequence":"additional","affiliation":[{"name":"Department of Radiology, Memorial Sloan Kettering Cancer Center , New York, NY 10065,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7456-3197","authenticated-orcid":false,"given":"Amita","family":"Shukla-Dave","sequence":"additional","affiliation":[{"name":"Department of Medical Physics, Memorial Sloan Kettering Cancer Center , New York, NY 10065,","place":["United States"]},{"name":"Department of Radiology, Memorial Sloan Kettering Cancer Center , New York, NY 10065,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2026,1,22]]},"reference":[{"key":"2026041223593491600_ubag002-B1","doi-asserted-by":"publisher","first-page":"2052","DOI":"10.3390\/tomography9060161","article-title":"A quantitative multiparametric MRI analysis platform for estimation of robust imaging biomarkers in clinical oncology","volume":"9","author":"LoCastro","year":"2023","journal-title":"Tomography"},{"key":"2026041223593491600_ubag002-B2","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1186\/s12859-018-2588-1","article-title":"MITK-ModelFit: a generic open-source framework for model fits and their exploration in medical imaging\u2014design, implementation and application on the example of DCE-MRI","volume":"20","author":"Debus","year":"2019","journal-title":"BMC Bioinformatics"},{"key":"2026041223593491600_ubag002-B3","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1186\/s12880-015-0062-3","article-title":"ROCKETSHIP: a flexible and modular software tool for the planning, processing and analysis of dynamic MRI studies","volume":"15","author":"Barnes","year":"2015","journal-title":"BMC Med Imaging"},{"key":"2026041223593491600_ubag002-B4","doi-asserted-by":"publisher","first-page":"1745","DOI":"10.1002\/jmri.27983","article-title":"Multi-site concordance of diffusion-weighted imaging quantification for assessing prostate cancer aggressiveness","volume":"55","author":"McGarry","year":"2022","journal-title":"J Magn Reson Imaging"},{"key":"2026041223593491600_ubag002-B5","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","year":"2023","journal-title":"Cancers (Basel)"},{"key":"2026041223593491600_ubag002-B6","doi-asserted-by":"publisher","first-page":"1980","DOI":"10.1001\/jama.2020.17598","article-title":"Bladder cancer: a review","volume":"324","author":"Lenis","year":"2020","journal-title":"JAMA"},{"key":"2026041223593491600_ubag002-B7","doi-asserted-by":"publisher","first-page":"7","DOI":"10.1186\/s13244-024-01884-5","article-title":"Multiparametric MRI and artificial intelligence in predicting and monitoring treatment response in bladder cancer","volume":"16","author":"Arita","year":"2025","journal-title":"Insights Imaging"},{"key":"2026041223593491600_ubag002-B8","doi-asserted-by":"publisher","first-page":"485","DOI":"10.1016\/j.eururo.2024.09.013","article-title":"The role of multiparametric magnetic resonance imaging and the vesical Imaging-Reporting and data system (VI-RADS) in the management of patients with bladder cancer: vision of the American College of Radiology VI-RADS Steering Committee","volume":"86","author":"Panebianco","year":"2024","journal-title":"Eur Urol"},{"key":"2026041223593491600_ubag002-B9","doi-asserted-by":"publisher","first-page":"103601","DOI":"10.1016\/j.critrevonc.2022.103601","article-title":"Artificial intelligence: a promising frontier in bladder cancer diagnosis and outcome prediction","volume":"171","author":"Borhani","year":"2022","journal-title":"Crit Rev Oncol Hematol"},{"key":"2026041223593491600_ubag002-B10","doi-asserted-by":"publisher","first-page":"2308","DOI":"10.3390\/diagnostics13132308","article-title":"Artificial intelligence in the advanced diagnosis of bladder cancer-comprehensive literature review and future advancement","volume":"13","author":"Ferro","year":"2023","journal-title":"Diagnostics (Basel)"},{"key":"2026041223593491600_ubag002-B11","doi-asserted-by":"publisher","first-page":"548","DOI":"10.1097\/RLI.0000000000000962","article-title":"Multiparametric MRI: from simultaneous rapid acquisition methods and analysis techniques using scoring, machine learning, radiomics, and deep learning to the generation of novel metrics","volume":"58","author":"Hagiwara","year":"2023","journal-title":"Invest Radiol"},{"key":"2026041223593491600_ubag002-B12","doi-asserted-by":"publisher","first-page":"044501","DOI":"10.1117\/1.JMI.10.4.044501","article-title":"Radiomic and deep learning characterization of breast parenchyma on full field digital mammograms and specimen radiographs: a pilot study of a potential cancer field effect","volume":"10","author":"Baughan","year":"2023","journal-title":"J Med Imaging (Bellingham)"},{"key":"2026041223593491600_ubag002-B13","doi-asserted-by":"publisher","first-page":"3759","DOI":"10.1007\/s00330-017-4758-3","article-title":"An evaluation of morphological and functional multi-parametric MRI sequences in classifying non-muscle and muscle invasive bladder cancer","volume":"27","author":"Panebianco","year":"2017","journal-title":"Eur Radiol"},{"key":"2026041223593491600_ubag002-B14","doi-asserted-by":"publisher","first-page":"102","DOI":"10.1593\/neo.81328","article-title":"Diffusion-weighted magnetic resonance imaging as a cancer biomarker: consensus and recommendations","volume":"11","author":"Padhani","year":"2009","journal-title":"Neoplasia"},{"key":"2026041223593491600_ubag002-B15","doi-asserted-by":"publisher","first-page":"2797","DOI":"10.1007\/s00261-024-04397-3","article-title":"Pictorial review of multiparametric MRI in bladder urothelial carcinoma with variant histology: pearls and pitfalls","volume":"49","author":"Arita","year":"2024","journal-title":"Abdom Radiol (NY)"},{"key":"2026041223593491600_ubag002-B16","doi-asserted-by":"publisher","first-page":"878","DOI":"10.4103\/0973-1482.138225","article-title":"The usefulness of diffusion-weighted magnetic resonance imaging in bladder cancer staging and functional analysis","volume":"10","author":"Yamada","year":"2014","journal-title":"J Cancer Res Ther"},{"key":"2026041223593491600_ubag002-B17","doi-asserted-by":"publisher","first-page":"109522","DOI":"10.1016\/j.ejrad.2021.109522","article-title":"Prediction of muscle invasion of bladder cancer: a comparison between DKI and conventional DWI","volume":"136","author":"Li","year":"2021","journal-title":"Eur J Radiol"},{"key":"2026041223593491600_ubag002-B18","doi-asserted-by":"publisher","first-page":"1088","DOI":"10.1002\/jmri.23770","article-title":"Extension of the intravoxel incoherent motion model to non-gaussian diffusion in head and neck cancer","volume":"36","author":"Lu","year":"2012","journal-title":"J Magn Reson Imaging"},{"key":"2026041223593491600_ubag002-B19","doi-asserted-by":"publisher","first-page":"223","DOI":"10.1002\/(SICI)1522-2586(199909)10:3&lt;223::AID-JMRI2&gt;3.0.CO;2-S","article-title":"Estimating kinetic parameters from dynamic contrast-enhanced T1-weighted MRI of a diffusable tracer: standardized quantities and symbols","volume":"10","author":"Tofts","year":"1999","journal-title":"J Magn Reson Imaging"},{"key":"2026041223593491600_ubag002-B20","doi-asserted-by":"publisher","first-page":"1028","DOI":"10.1002\/nbm.2996","article-title":"Model selection in measures of vascular parameters using dynamic contrast-enhanced MRI: experimental and clinical applications","volume":"26","author":"Ewing","year":"2013","journal-title":"NMR Biomed"},{"key":"2026041223593491600_ubag002-B21","doi-asserted-by":"publisher","first-page":"e4166","DOI":"10.1002\/nbm.4166","article-title":"Dynamic contrast-enhanced MRI model selection for predicting tumor aggressiveness in papillary thyroid cancers","volume":"33","author":"Paudyal","year":"2020","journal-title":"NMR Biomed"},{"key":"2026041223593491600_ubag002-B22","doi-asserted-by":"publisher","first-page":"47","DOI":"10.1016\/j.jneumeth.2016.03.001","article-title":"The first step for neuroimaging data analysis: DICOM to NIfTI conversion","volume":"264","author":"Li","year":"2016","journal-title":"J Neurosci Methods"},{"key":"2026041223593491600_ubag002-B23","doi-asserted-by":"publisher","first-page":"3342","DOI":"10.1109\/EMBC.2016.7591443","article-title":"ITK-SNAP: an interactive tool for semi-automatic segmentation of multi-modality biomedical images","volume":"2016","author":"Yushkevich","year":"2016","journal-title":"Annu Int Conf IEEE Eng Med Biol Soc"},{"key":"2026041223593491600_ubag002-B24","author":"Raffelt","year":"2017"},{"key":"2026041223593491600_ubag002-B25","author":"He","year":"2016"},{"key":"2026041223593491600_ubag002-B26","article-title":"Imagenet classification with deep convolutional neural networks","volume":"25","author":"Krizhevsky","year":"2012","journal-title":"Adv Neural Inf Process Syst"},{"key":"2026041223593491600_ubag002-B27"},{"key":"2026041223593491600_ubag002-B28","author":"Huang","year":"2017"},{"key":"2026041223593491600_ubag002-B29","author":"Liu","year":"2021"},{"key":"2026041223593491600_ubag002-B30","volume-title":"International Conference on Machine Learning","author":"Tan","year":"2019"},{"key":"2026041223593491600_ubag002-B31","volume-title":"International Conference on Machine Learning","author":"Tan","year":"2021"},{"key":"2026041223593491600_ubag002-B32","author":"Szegedy","year":"2015"},{"key":"2026041223593491600_ubag002-B33","author":"Iandola","year":"2016"},{"key":"2026041223593491600_ubag002-B34","first-page":"1314","author":"Howard","year":"2019"},{"key":"2026041223593491600_ubag002-B35"},{"key":"2026041223593491600_ubag002-B36","author":"Radosavovic","year":"2020"},{"key":"2026041223593491600_ubag002-B37","author":"Ma","year":"2018"},{"key":"2026041223593491600_ubag002-B38","author":"Team P","year":"2024"},{"key":"2026041223593491600_ubag002-B39","doi-asserted-by":"publisher","first-page":"507","DOI":"10.1186\/s13104-020-05343-4","article-title":"Can pre-trained convolutional neural networks be directly used as a feature extractor for video-based neonatal sleep and wake classification?","volume":"13","author":"Awais","year":"2020","journal-title":"BMC Res Notes"},{"key":"2026041223593491600_ubag002-B40","doi-asserted-by":"publisher","first-page":"1441","DOI":"10.1109\/JBHI.2021.3073632","article-title":"A hybrid DCNN-SVM model for classifying neonatal sleep and wake states based on facial expressions in video","volume":"25","author":"Awais","year":"2021","journal-title":"IEEE J Biomed Health Inform"},{"key":"2026041223593491600_ubag002-B41","doi-asserted-by":"publisher","first-page":"584","DOI":"10.1038\/jcbfm.1985.87","article-title":"Graphical evaluation of blood-to-brain transfer constants from multiple-time uptake data. Generalizations","volume":"5","author":"Patlak","year":"1985","journal-title":"J Cereb Blood Flow Metab"},{"key":"2026041223593491600_ubag002-B42","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/jcbfm.1983.1","article-title":"Graphical evaluation of blood-to-brain transfer constants from multiple-time uptake data","volume":"3","author":"Patlak","year":"1983","journal-title":"J Cereb Blood Flow Metab"},{"key":"2026041223593491600_ubag002-B43","doi-asserted-by":"publisher","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"},{"key":"2026041223593491600_ubag002-B44","doi-asserted-by":"publisher","first-page":"8978","DOI":"10.1038\/s41598-019-45223-x","article-title":"A hybrid gene selection method based on ReliefF and ant colony optimization algorithm for tumor classification","volume":"9","author":"Sun","year":"2019","journal-title":"Sci Rep"},{"key":"2026041223593491600_ubag002-B45","doi-asserted-by":"publisher","first-page":"143","DOI":"10.11613\/bm.2013.018","article-title":"The chi-square test of independence","volume":"23","author":"McHugh","year":"2013","journal-title":"Biochem Med (Zagreb)"},{"key":"2026041223593491600_ubag002-B46","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1016\/0169-7439(89)80095-4","article-title":"Analysis of variance (ANOVA)","volume":"6","author":"St","year":"1989","journal-title":"Chemometrics and Intelligent Laboratory Systems"},{"key":"2026041223593491600_ubag002-B47","first-page":"1","author":"McKight","year":"2010"},{"key":"2026041223593491600_ubag002-B48","doi-asserted-by":"crossref","DOI":"10.1007\/978-0-387-77242-4","volume-title":"Support Vector Machines","author":"Steinwart","year":"2008"},{"key":"2026041223593491600_ubag002-B49","doi-asserted-by":"publisher","first-page":"448","DOI":"10.1002\/wics.1278","article-title":"Decision trees","volume":"5","author":"De Ville","year":"2013","journal-title":"WIREs Computational Stats"},{"key":"2026041223593491600_ubag002-B50","author":"Fix","year":"1985"},{"key":"2026041223593491600_ubag002-B51","doi-asserted-by":"publisher","first-page":"2161","DOI":"10.1016\/j.ejrad.2013.08.008","article-title":"Dynamic contrast-enhanced MRI in patients with muscle-invasive transitional cell carcinoma of the bladder can distinguish between residual tumour and post-chemotherapy effect","volume":"82","author":"Donaldson","year":"2013","journal-title":"Eur J Radiol"},{"key":"2026041223593491600_ubag002-B52","doi-asserted-by":"publisher","first-page":"1","DOI":"10.18383\/j.tom.2018.00045","article-title":"QIN benchmarks for clinical translation of quantitative imaging tools","volume":"5","author":"Farahani","year":"2019","journal-title":"Tomography"},{"key":"2026041223593491600_ubag002-B53","doi-asserted-by":"publisher","first-page":"e2300180","DOI":"10.1200\/CCI.23.00180","article-title":"Deep learning features can improve radiomics-based prostate cancer aggressiveness prediction","volume":"8","author":"Rodrigues","year":"2024","journal-title":"JCO Clin Cancer Inform."},{"key":"2026041223593491600_ubag002-B54","doi-asserted-by":"publisher","first-page":"85","DOI":"10.1016\/j.compbiomed.2019.04.018","article-title":"Deep learning for identifying radiogenomic associations in breast cancer","volume":"109","author":"Zhu","year":"2019","journal-title":"Comput Biol Med"},{"key":"2026041223593491600_ubag002-B55","doi-asserted-by":"publisher","DOI":"10.3390\/s21062222","article-title":"MRI-Based brain tumor classification using ensemble of deep features and machine learning classifiers","volume":"21","author":"Kang","year":"2021","journal-title":"Sensors (Basel)"},{"key":"2026041223593491600_ubag002-B56","doi-asserted-by":"publisher","first-page":"472","DOI":"10.3390\/bioengineering11050472","article-title":"Preoperative molecular subtype classification prediction of ovarian cancer based on multi-parametric magnetic resonance imaging multi-sequence feature fusion network","volume":"11","author":"Du","year":"2024","journal-title":"Bioengineering"},{"key":"2026041223593491600_ubag002-B57","first-page":"1001","article-title":"Comparison of cystoscopy with diffusion-weighted magnetic resonance images used in the diagnosis and follow-up of patients with bladder tumors","volume":"11","author":"Ceylan","year":"2010","journal-title":"Asian Pac J Cancer Prev"},{"key":"2026041223593491600_ubag002-B58","doi-asserted-by":"publisher","first-page":"1166","DOI":"10.1016\/j.urolonc.2014.04.019","article-title":"Prospective evaluation of diffusion-weighted MRI of the bladder as a biomarker for prediction of bladder cancer aggressiveness","volume":"32","author":"Sevcenco","year":"2014","journal-title":"Urol Oncol"},{"key":"2026041223593491600_ubag002-B59","first-page":"43","article-title":"Non-muscle-invasive bladder cancer: overview and contemporary treatment landscape of neoadjuvant chemoablative therapies","volume":"22","author":"Matulewicz","year":"2020","journal-title":"Rev Urol"},{"key":"2026041223593491600_ubag002-B60","doi-asserted-by":"publisher","first-page":"2013","DOI":"10.1016\/j.ejrad.2014.08.012","article-title":"Contrast-enhanced dynamic and diffusion-weighted MR imaging at 3.0T to assess aggressiveness of bladder cancer","volume":"83","author":"Zhou","year":"2014","journal-title":"Eur J Radiol"},{"key":"2026041223593491600_ubag002-B61","doi-asserted-by":"publisher","first-page":"219","DOI":"10.1002\/mrm.22774","article-title":"Comparison of dynamic contrast-enhanced MRI and dynamic contrast-enhanced CT biomarkers in bladder cancer","volume":"66","author":"Naish","year":"2011","journal-title":"Magn Reson Med"}],"container-title":["BJR|Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bjrai\/advance-article-pdf\/doi\/10.1093\/bjrai\/ubag002\/66540224\/ubag002.pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bjrai\/article-pdf\/3\/1\/ubag002\/66540224\/ubag002.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bjrai\/article-pdf\/3\/1\/ubag002\/66540224\/ubag002.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T03:59:53Z","timestamp":1776052793000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bjrai\/article\/doi\/10.1093\/bjrai\/ubag002\/8438344"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1]]},"references-count":61,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2026,1,23]]}},"URL":"https:\/\/doi.org\/10.1093\/bjrai\/ubag002","relation":{},"ISSN":["2976-8705"],"issn-type":[{"value":"2976-8705","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2026,1]]},"published":{"date-parts":[[2026,1]]},"article-number":"ubag002"}}