{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,22]],"date-time":"2026-07-22T15:49:17Z","timestamp":1784735357055,"version":"3.55.0"},"reference-count":36,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,4,15]],"date-time":"2026-04-15T00:00:00Z","timestamp":1776211200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Oncol."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>The diagnostic challenges inherent in prostate-specific antigen (PSA) levels between 4\u201310 ng\/mL represent a critical clinical dilemma, with only 25\u201330% of patients harboring clinically significant prostate cancer, leading to substantial rates of unnecessary biopsies and associated morbidity.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>To develop and validate a multimodal convolutional neural network integrating T2-weighted imaging, diffusion-weighted imaging, apparent diffusion coefficient maps, and clinical parameters for enhanced detection of clinically significant prostate cancer in the PSA gray zone.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>This retrospective cohort study analyzed 305 patients with PSA levels 4\u201310 ng\/mL who underwent multiparametric MRI and subsequent biopsy confirmation. A novel multimodal CNN architecture based on modified U-Net with ResNet-50 backbone was developed, incorporating comprehensive fusion strategies. Decision curve analysis was performed to evaluate clinical utility across a range of threshold probabilities.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The proposed multimodal CNN achieved superior diagnostic performance with an area under the curve of 0.913 (95% CI: 0.851\u20130.975), sensitivity of 85.3% (71.4\u201394.2%), specificity of 90.9% (78.3\u201397.5%), and overall accuracy of 88.5% (78.2\u201395.1%), significantly outperforming PSA alone (AUC 0.592, p&amp;lt;0.001) and PI-RADS assessment (AUC 0.694, p&amp;lt;0.001). Nested 5-fold cross-validation demonstrated consistent performance across folds (AUC range: 0.891\u20130.928), while extended bootstrap validation with 5,000 iterations confirmed robust stability (AUC standard deviation: 0.032). Inter-reader agreement between the model and expert radiologists demonstrated excellent concordance (\u03ba=0.871, 95% CI: 0.831\u20130.911). Decision curve analysis confirmed a consistently superior net benefit for the multimodal CNN across clinically relevant threshold probabilities.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>The multimodal deep learning approach represents a paradigm shift in non-invasive prostate cancer detection, potentially reducing unnecessary biopsies by 40\u201350% while maintaining exceptional sensitivity for clinically significant disease. Decision curve analysis substantiates the clinical utility of this approach across a broad range of decision thresholds.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fonc.2026.1763766","type":"journal-article","created":{"date-parts":[[2026,4,15]],"date-time":"2026-04-15T04:42:52Z","timestamp":1776228172000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Construction and validation of a multimodal MRI-based deep learning model for early differential diagnosis of prostate cancer in the PSA gray zone: a retrospective cohort study"],"prefix":"10.3389","volume":"16","author":[{"given":"Zuliang","family":"Xu","sequence":"first","affiliation":[{"name":"Department of Radiology, Taizhou Central Hospital (Taizhou University Hospital)","place":["Taizhou, Zhejiang, 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