ArticleJournal of magnetic resonance imaging : JMRI2026
AI-Assisted Prostate Cancer Diagnosis Using Biparametric MRI and PI-RADS v2.1: Performance Comparison Between Novice-Level and Experienced Readers.
Article in Journal of magnetic resonance imaging : JMRI, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
- Editorial for "AI-Assisted Prostate Cancer Diagnosis Using Biparametric MRI and PI-RADS v2.1: Performance Comparison Between Novice-Level and Experienced Readers".Journal of magnetic resonance imaging : JMRI · 2026Article
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9 authors.
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Abstract
backgroundDespite promising results of artificial intelligence (AI) in prostate cancer (PCa) detection, its impact on biparametric MRI (bpMRI) interpretation remains uncertain, especially for readers with limited experience. PURPOSE: To evaluate the effect of AI software assistance on prostate bpMRI interpretation by readers with different levels of prostate MRI experience. STUDY TYPE: Retrospective. POPULATION: Six hundred and forty-six male patients, including 297 with PCa. FIELD STRENGTH/SEQUENCE: 3.0 T; T2-weighted imaging using fast spin echo sequence, diffusion-weighted imaging using single-shot echo-planar imaging. ASSESSMENT: Two experienced readers (8 and 10 years of prostate MRI experience) and two novice-level readers (2 years of general radiology experience; 20-50 prior prostate MRI cases) assessed all examinations twice, without and with AI software (uAI, United Imaging) assistance, in counterbalanced orders with a 4-week washout interval. Lesions were scored using Prostate Imaging Reporting and Data System (PI-RADS) v2.1 at ≥ 3 and ≥ 4 thresholds. Histopathology was the reference standard. The primary analysis defined cancer as International Society of Urological Pathology (ISUP) grade group ≥ 1 (Gleason score ≥ 6); sensitivity analysis defined clinically significant cancer as ISUP grade group ≥ 2. STATISTICAL TESTS: Generalized Estimating Equations were used for clustered data. Receiver operating characteristic (ROC) analysis with the Obuchowski-Rockette model was used to compare the area under the ROC curve (AUC). Cohen's κ assessed inter-reader agreement; two-sided p < 0.05 indicated significance.
resultsFor ISUP ≥ 1, uAI increased novice-level/experienced-reader AUCs (0.684-0.744; 0.757-0.794). At PI-RADS ≥ 3, novice-level sensitivity/specificity significantly improved (0.71-0.79; 0.46-0.58). Experienced-reader sensitivity gains were nonsignificant (p = 0.344/0.291). For ISUP ≥ 2 at ≥ 3, all-reader sensitivity/specificity increased (0.76-0.82; 0.47-0.57). Novice-level κ increased at ≥ 3/≥ 4 (0.582-0.700; 0.654-0.741). DATA
conclusionuAI assistance improved diagnostic performance, with multi-metric improvements in novice-level readers. LEVEL OF EVIDENCE: 2: TECHNICAL EFFICACY: Stage 3.
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