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

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...

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Bibliographic Details
Published in:Journal of magnetic resonance imaging 2023-05, Vol.57 (5), p.1352-1364
Main Authors: 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
Format: Article
Language:English
Subjects:
Artificial Intelligence
biparametric MRI
Clinical significance
clinically significant prostate cancer
deep learning
Diagnosis
Diagnostic systems
Diffusion coefficient
Diffusion Magnetic Resonance Imaging - methods
Field strength
Heterogeneity
Humans
Image segmentation
Magnetic resonance imaging
Magnetic Resonance Imaging - methods
Male
Medical imaging
Prostate cancer
Prostatic Neoplasms - pathology
Retrospective Studies
Statistical analysis
Statistical tests
the Prostate Imaging Reporting and Data System
Training
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Summary: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 (T2WI), 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 
ISSN:1053-1807
1522-2586
DOI:10.1002/jmri.28427