Related Experiment Video
Updated: Jun 19, 2026
A Cognitive Fusion-guided Prostate Biopsy Using Multiparametric Magnetic Resonance Imaging and Transrectal Ultrasound
Published on: March 21, 2025
AI-Assisted Prostate Cancer Diagnosis Using Biparametric MRI and PI-RADS v2.1: Performance Comparison Between
Kexin Li1, Shaonan Mi1, Lu Chen1
1Department of Radiology, The Second Affiliated Hospital of Harbin Medical University, Harbin, China.
Background:
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.
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.
Results:
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).
Data Conclusion:
uAI assistance improved diagnostic performance, with multi-metric improvements in novice-level readers.
Technical Efficacy:
Stage 3.
