TY - JOUR
T1 - Performance of Artificial Intelligence-Aided Diagnosis System for Clinically Significant Prostate Cancer with MRI
T2 - A Diagnostic Comparison Study
AU - Jiang, Ke Wen
AU - Song, Yang
AU - Hou, Ying
AU - Zhi, Rui
AU - Zhang, Jing
AU - Bao, Mei Ling
AU - Li, Hai
AU - Yan, Xu
AU - Xi, Wei
AU - Zhang, Cheng Xiu
AU - Yao, Ye Feng
AU - Yang, Guang
AU - Zhang, Yu Dong
N1 - Publisher Copyright:
© 2022 International Society for Magnetic Resonance in Medicine.
PY - 2023/5
Y1 - 2023/5
N2 - 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 < 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.
AB - 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 < 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.
KW - artificial intelligence
KW - biparametric MRI
KW - clinically significant prostate cancer
KW - deep learning
KW - the Prostate Imaging Reporting and Data System
UR - https://www.scopus.com/pages/publications/85138646659
U2 - 10.1002/jmri.28427
DO - 10.1002/jmri.28427
M3 - 文章
C2 - 36222324
AN - SCOPUS:85138646659
SN - 1053-1807
VL - 57
SP - 1352
EP - 1364
JO - Journal of Magnetic Resonance Imaging
JF - Journal of Magnetic Resonance Imaging
IS - 5
ER -
