Performance of Artificial Intelligence-Aided Diagnosis System for Clinically Significant Prostate Cancer with MRI: A Diagnostic Comparison Study

Abstract

Background: The high level of expertise required for accurate interpretation of prostate MRI.

Purpose: To develop and test an artificial intelligence (AI) system for diagnosis of clinically significant prostate cancer (CsPC) with MRI.

Study type: Retrospective.

Subjects: One thousand two hundred thirty patients from derivation cohort between Jan 2012 and Oct 2019, and 169 patients from a publicly available data (U-Net: 423 for training/validation and 49 for test and TrumpeNet: 820 for training/validation and 579 for test).

Field strength/sequence: 3.0T/scanners, T₂ -weighted imaging (T₂ WI), diffusion-weighted imaging, and apparent diffusion coefficient map.

Assessment: Close-loop AI system was trained with an Unet for prostate segmentation and a TrumpetNet for CsPC detection. Performance of AI was tested in 410 internal and 169 external sets against 24 radiologists categorizing into junior, general and subspecialist group. Gleason score >6 was identified as CsPC at pathology.

Statistical tests: Area under the receiver operating characteristic curve (AUC-ROC); Delong test; Meta-regression I² analysis.

Results: In average, for internal test, AI had lower AUC-ROC than subspecialists (0.85 vs. 0.92, P < 0.05), and was comparable to junior (0.84, P = 0.76) and general group (0.86, P = 0.35). For external test, both AI (0.86) and subspecialist (0.86) had higher AUC than junior (0.80, P < 0.05) and general reader (0.83, P < 0.05). In individual, it revealed moderate diagnostic heterogeneity in 24 readers (Mantel-Haenszel I² = 56.8%, P < 0.01), and AI outperformed 54.2% (13/24) of readers in summary ROC analysis. In multivariate test, Gleason score, zonal location, PI-RADS score and lesion size significantly impacted the accuracy of AI; while effect of data source, MR device and parameter settings on AI performance is insignificant (P > 0.05).

Data conclusion: Our AI system can match and to some case exceed clinicians for the diagnosis of CsPC with prostate MRI.

Evidence level: 3 TECHNICAL EFFICACY: Stage 2.

Keywords: artificial intelligence; biparametric MRI; clinically significant prostate cancer; deep learning; the Prostate Imaging Reporting and Data System.