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In Journal of magnetic resonance imaging : JMRI

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.


Jiang Ke-Wen, Song Yang, Hou Ying, Zhi Rui, Zhang Jing, Bao Mei-Ling, Li Hai, Yan Xu, Xi Wei, Zhang Cheng-Xiu, Yao Ye-Feng, Yang Guang, Zhang Yu-Dong


artificial intelligence, biparametric MRI, clinically significant prostate cancer, deep learning, the Prostate Imaging Reporting and Data System