A Prostate Imaging‐Reporting and Data System version 2.1‐based predictive model for clinically significant prostate cancer diagnosis

作者：David G. Gelikman, William S. Azar, Enis C. Yılmaz, Yue Lin, Luke Shumaker, Andrew M. Fang, Stephanie A. Harmon, Erich P. Huang, Sahil H. Parikh, Jason A. Hyman, Kyle Schuppe, Jeffrey W. Nix, Samuel J. Galgano, Maria J. Merino, Peter L. Choyke, Sandeep Gurram, Bradford J. Wood, Soroush Rais‐Bahrami, Peter A. Pinto, Barış Türkbey · 发表于：British Journal of Urology · 年份：2024 · DOI：10.1111/bju.16616 · 被引用次数：5 · 研究领域：Prostate Cancer Diagnosis and Treatment、Prostate Cancer Treatment and Research、Advanced Radiotherapy Techniques

OBJECTIVES: To develop and validate a Prostate Imaging-Reporting and Data System (PI-RADS) version 2.1 (v2.1)-based predictive model for diagnosis of clinically significant prostate cancer (csPCa), integrating clinical and multiparametric magnetic resonance imaging (mpMRI) data, and compare its performance with existing models. PATIENTS AND METHODS: We retrospectively analysed data from patients who underwent prospective mpMRI assessment using the PI-RADS v2.1 scoring system and biopsy at our institution between April 2019 and December 2023. A 'Clinical Baseline' model using patient demographics and laboratory results and an 'MRI Added' model additionally incorporating PI-RADS v2.1 scores and prostate volumes were created and validated on internal and external patients. Both models were compared against two previously published MRI-based algorithms for csPCa using area under the receiver operating characteristic curve (AUC) and decision curve analysis. RESULTS: 0.68) (P < 0.001). The 'MRI Added' model also showed higher net benefits across various clinical threshold probabilities and compared to a 'biopsy all' approach, it reduced unnecessary biopsies (defined as biopsies without Gleason Grade Group ≥2 csPCa) by 27% in the internal cohort and 10% in the external cohort at a risk threshold of 25%. However, there was no significant difference in predictive ability and reduction in unnecessary biopsies between our model and comparative ones developed for PI-RADS v2 and v1. CONCL...