Despite 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.
To evaluate the effect of AI software assistance on prostate bpMRI interpretation by readers with different levels of prostate MRI experience.
Retrospective.
Six hundred and forty-six male patients, including 297 with PCa.
3.0 T; T2-weighted imaging using fast spin echo sequence, diffusion-weighted imaging using single-shot echo-planar imaging.
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.
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.
For 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).
uAI assistance improved diagnostic performance, with multi-metric improvements in novice-level readers.
Stage 3.
This study tested whether artificial intelligence software could help doctors read prostate MRI scans more consistently and accurately. The researchers studied 646 men with 730 lesions. Two experienced doctors and two doctors with limited prostate MRI experience reviewed each case without and with artificial intelligence support. The software improved several measures of cancer detection, especially for doctors with limited experience, and increased agreement between these doctors. Additional analyses showed that doctors rarely changed correct judgments to match incorrect artificial intelligence outputs, whereas incorrect judgments were more often corrected. These findings support artificial intelligence as a decision‐support tool for prostate MRI.
Journal of magnetic resonance imaging : JMRI. 2026 Jun 17 [Epub ahead of print]
Kexin Li, Shaonan Mi, Lu Chen, Miaomiao Jiang, Bingyan Wang, Yuanyuan Huang, Yiqiu Wang, Guoxuan Fei, Kuang Fu
Department of Radiology, The Second Affiliated Hospital of Harbin Medical University, Harbin, China., Harbin Medical University, Harbin, China.