{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,4]],"date-time":"2026-08-04T12:39:14Z","timestamp":1785847154763,"version":"3.56.0"},"reference-count":31,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2025,12,8]],"date-time":"2025-12-08T00:00:00Z","timestamp":1765152000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["82372035"],"award-info":[{"award-number":["82372035"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Sword Action Talent Program","award":["2024RCYGXXP"],"award-info":[{"award-number":["2024RCYGXXP"]}]},{"name":"Joint Founding Project of Innovation Research Institute of Xijing Hospital","award":["LHJJ24YG06"],"award-info":[{"award-number":["LHJJ24YG06"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Bioengineering"],"abstract":"<jats:p>Background: The Vesical Imaging-Reporting and Data System (VI-RADS) has limited diagnostic accuracy in distinguishing non-muscle-invasive bladder cancer (NMIBC) within VI-RADS categories 2 and 3, despite its value for overall NMIBC assessment. Dynamic contrast-enhanced MRI (DCE-MRI), which reflects tumor vascularity, holds promise for improving these challenging cases but remains underutilized due to unexploited spatiotemporal information. Methods: We developed a deep learning model to comprehensively quantify spatiotemporal features from multiphase DCE-MRI in 184 patients with VI-RADS 2 or 3 (training: n = 115, validation: n = 20, testing: n = 49). The model integrated multiscale feature extraction and contextual attention mechanisms to enhance diagnostic performance. Results: The model outperformed established benchmarks (e.g., VGG, ResNet) and the conventional VI-RADS \u2264 2 threshold (sensitivity: 0.67 for NMIBC), achieving a sensitivity of 0.90 (95% CI: 0.81\u20130.96) for NMIBC and an area under the curve (AUC) of 0.82 (95% CI: 0.75\u20130.89) for overall classification. Visualizations confirmed its ability to identify key spatiotemporal patterns linked to muscle invasion. Conclusions: By leveraging comprehensive spatiotemporal information from DCE-MRI, our deep learning model significantly improves NMIBC diagnosis in VI-RADS 2\/3 cases, offering a clinically valuable tool to address the limitations of current VI-RADS assessment.<\/jats:p>","DOI":"10.3390\/bioengineering12121338","type":"journal-article","created":{"date-parts":[[2025,12,8]],"date-time":"2025-12-08T14:52:24Z","timestamp":1765205544000},"page":"1338","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Beyond VI-RADS Uncertainty: Leveraging Spatiotemporal DCE-MRI to Predict Bladder Cancer Muscle Invasion"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-5311-1011","authenticated-orcid":false,"given":"Minghui","family":"Song","sequence":"first","affiliation":[{"name":"School of Biomedical Engineering, Air Force Medical University, Xi\u2019an 710032, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haonan","family":"Ren","sequence":"additional","affiliation":[{"name":"School of Biomedical Engineering, Air Force Medical University, Xi\u2019an 710032, China"},{"name":"School of Information and Communication Engineering, Hainan University, Haikou 570228, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8910-2617","authenticated-orcid":false,"given":"Lijuan","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Life Science and Technology, Xi\u2019an Jiaotong University, Xi\u2019an 710049, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-4021-8834","authenticated-orcid":false,"given":"Yihang","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Biomedical Engineering, Air Force Medical University, Xi\u2019an 710032, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7051-9823","authenticated-orcid":false,"given":"Xing","family":"Tang","sequence":"additional","affiliation":[{"name":"Department of Radiology, Xijing Hospital, Air Force Medical University, Xi\u2019an 710032, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9089-7736","authenticated-orcid":false,"given":"Huanjun","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Radiology, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou 510080, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0267-7328","authenticated-orcid":false,"given":"Yan","family":"Guo","sequence":"additional","affiliation":[{"name":"Department of Radiology, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou 510080, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0984-5128","authenticated-orcid":false,"given":"Yang","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Biomedical Engineering, Air Force Medical University, Xi\u2019an 710032, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4181-7239","authenticated-orcid":false,"given":"Hongbing","family":"Lu","sequence":"additional","affiliation":[{"name":"School of Biomedical Engineering, Air Force Medical University, Xi\u2019an 710032, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3707-1104","authenticated-orcid":false,"given":"Xiaopan","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Biomedical Engineering, Air Force Medical University, Xi\u2019an 710032, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,12,8]]},"reference":[{"key":"ref_1","first-page":"229","article-title":"Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries","volume":"74","author":"Bray","year":"2024","journal-title":"CA Cancer J. 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