ArticleAbdominal radiology (New York)2026
Deep learning for clinically significant prostate cancer detection on MRI: a systematic review, HSROC meta-analysis, and direct comparison with PI-RADS-based interpretation.
Article in Abdominal radiology (New York), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
objectivesTo estimate patient-level diagnostic accuracy of deep learning (DL) for MRI-based detection of clinically significant prostate cancer (csPCa), assess heterogeneity and clinical-readiness signals, and compare DL-alone, PI-RADS-alone, and AI-assisted/DL + PI-RADS interpretation where direct comparator data were available.
methodsFollowing PRISMA-DTA, MEDLINE, Embase, and Web of Science were searched from 2010 to June 2025 for studies reporting patient-level 2 × 2 diagnostic accuracy data for DL applied to prostate MRI. Risk of bias was assessed using QUADAS-2. Pooled sensitivity and specificity were estimated using bivariate random-effects and HSROC models, with prespecified subgroup, meta-regression, and sensitivity analyses. Deeks' funnel plot asymmetry test assessed publication bias and small-study effects. A secondary direct three-way comparative analysis was performed in studies reporting DL-alone, PI-RADS-alone, and AI-assisted/DL + PI-RADS data within the same or closely matched cohorts. AI-specific reporting and clinical-readiness signals were mapped using items adapted from STARD-AI, CLAIM, and DECIDE-AI.
resultsThirty-six studies including 9,411 patients were included. Pooled sensitivity was 0.91 (95% CI, 0.89-0.93), specificity was 0.55 (95% CI, 0.46-0.64), LR + was 2.04, and LR - was 0.16. Sensitivity was relatively consistent, whereas specificity varied widely, with a broad HSROC prediction region indicating limited transportability of pooled specificity. Deeks' test showed no significant funnel plot asymmetry (p = 0.393). Sensitivity analyses excluding MRI + clinical-variable hybrid models and small cohorts produced similar estimates. In nine directly comparative studies, sensitivity was similar across groups, while specificity was highest for AI-assisted/DL + PI-RADS and lowest for PI-RADS-alone.
conclusionDL for prostate MRI shows high sensitivity and low LR-, supporting a rule-out or assistive role. However, moderate and variable specificity limits stand-alone rule-in use. Combined AI/DL + PI-RADS workflows may reduce false positives, but prospective validation, calibration, interpretability evaluation, and patient-level safety studies are needed.
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