External validation and comparison of magnetic resonance imaging-based predictive models for clinically significant prostate cancer

• Published in: Urologic oncology Vol. 39; no. 11; pp. 783.e1 - 783.e10
• Main Authors: Lee, Han Jie, Lee, Alvin, Yang, Xin Yan, Law, Yan Mee, Huang, Hong Hong, Lau, Weber KO, Lee, Lui Shiong, Ho, Henry SS, Cheng, Christopher WS, Yuen, John SP, Tay, Kae Jack, Chen, Kenneth
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.11.2021
• Subjects: Aged; Diagnosis; Humans; Magnetic resonance imaging; Male; Middle Aged; Multiparametric Magnetic Resonance Imaging - methods; Prostate biopsy; Prostate cancer; Prostatic Neoplasms - diagnostic imaging; Retrospective Studies; Risk Assessment - methods; Risk calculator; Diagnosis; Magnetic resonance imaging; Prostate biopsy; Prostate cancer; Risk calculator
• ISSN: 1078-1439; 1873-2496; 1873-2496
• DOI: 10.1016/j.urolonc.2021.03.003

•Novel multiparametric magnetic resonance imaging (mpMRI)-based risk models demonstrate improved diagnostic accuracy for clinically significant prostate cancer.•These models show excellent discrimination even in an independent Asian population.•Superior clinical utility was provided by the risk models compared to the use of mpMRI findings alone.•The best performing model allowed 39% of unnecessary prostate biopsies to be avoided while missing only 4% of clinically significant prostate cancer.•These models are easy to apply in practice, and should be considered over the use of mpMRI alone. Several multiparametric magnetic resonance imaging (mpMRI)-based models have been developed with significant improvements in diagnostic accuracy for clinically significant prostate cancer (csCaP), but lack proper external validation. We therefore sought to externally validate and compare all published mpMRI-based csCaP risk prediction models in an independent Asian population. A total of 449 men undergoing combined transperineal fusion-targeted/systematic prostate biopsy at our specialist center between 2015 to 2019 were retrospectively analyzed. csCaP was defined as lesions with ISUP (International Society of Urological Pathology) grade group ≥2. The performance of 6 mpMRI-based risk models (MRI-ERSPC-3/4, Distler, Radtke, Mehralivand, van Leeuwen and He) were evaluated in terms of discrimination, calibration and clinical utility, using area under the receiver operating characteristic curve (AUC), calibration curves and decision curve analyses. A total of 202 (45%) subjects were diagnosed with csCaP. All models demonstrated excellent accuracy with AUCs ranging from 0.75 to 0.86, and most significantly outperformed mpMRI PIRADSv2.0 (Prostate Imaging Reporting and Data System version 2.0) alone. The models by Mehralivand and He showed good calibration to our validation population, with respective intercepts of -0.08 and -0.84. All models were nevertheless recalibrated to the csCaP prevalence in our population for analysis. Decision curve analysis showed that above a threshold probability of 10%, all mpMRI-based models demonstrated superior net benefit compared to mpMRI PIRADSv2.0 or a biopsy-all-men strategy. The van Leeuwen model had the greatest net benefit, avoiding 39% of unnecessary biopsies while missing only 4% of csCaP, at a threshold probability of 15%. The mpMRI-based risk models demonstrate excellent discrimination and clinical utility and are easy to apply in practice, suggesting that individualized risk-based approaches can be considered over mpMRI alone to avoid unnecessary biopsies.