Navigating Uncertainty in MRI Diagnosis: A Human‐ AI Collaborative Strategy for Stratifying Clinically Significant Prostate Cancer

Background Deep learning (DL) methods have shown potential for predicting clinically significant prostate cancer (csPCa), but radiologists often face challenges in effectively leveraging these techniques for csPCa prediction. Purpose To develop an automated DL model based on biparametric‐MRI (bpMRI) and propose a human‐machine collaborative strategy for predicting csPCa. Study Type Retrospective. Population A total of 4305 patients were enrolled. Centers 1–2 and 4–7 comprised the training (2437 patients, mean age 68 ± 8) and the internal validation (581 patients, mean age 67 ± 8) cohorts; Centers 8–10 comprised the external validation cohort 1 (622 patients, mean age 71 ± 8), and Center 3 comprised the external validation cohort 2 (665 patients, age not available). Field Strength/Sequence T2‐weighted imaging (T2WI) using fast or turbo spin echo and diffusion‐weighted imaging (DWI) using single‐shot echo planar imaging were acquired at 1.5 and 3 T. Assessment A DL model (UFormer) including prostate segmentation and csPCa prediction was constructed using bpMRI. Its performance was evaluated in two external validation cohorts (EVCs) and compared with that of radiologists. Further, a UFormer‐radiologist collaborative predictive strategy was proposed. Statistical Tests Area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, DeLong test, and McNemar test. p < 0.05 was considered significant. Results Compared with radiologists' Prostate Imaging Reporting and Data System (PI‐RADS) assessment, UFormer‐combined radiologists showed significantly higher AUC and accuracy of 0.918 ± 0.012 and 0.857 ± 0.014 for the less‐experienced radiologists, 0.931 ± 0.010 and 0.870 ± 0.014 for the more‐experienced radiologists, respectively, due to greatly increasing specificity by 121.7% for the less‐experienced radiologists and 60.2% for the more‐experienced radiologists in EVC1. Additionally, UFormer identified 86.5% and 93.9% of non‐csPCa patients, who had been interpreted originally as PI‐RADS 3 by more‐ and less‐experienced radiologists, respectively. Data Conclusions UFormer enhanced the predictive performance of radiologists and narrowed performance gaps between experience levels. The UFormer‐radiologist collaborative paradigm combined model advantages with PI‐RADS assessment, providing a strategy for clinical application. Evidence Level 4. Technical Efficacy Stage 2.