Development and validation of a multimodal artificial intelligence-based model for predicting post-prostatectomy treatment outcomes from baseline biparametric prostate magnetic resonance imaging

作者：Benjamin Simon, Esra Akçiçek, S J Harmon, Lei Clifton, Anshul Thakur, Sandeep Gurram, David Clifton, B Wood, Ali Devrim Karaosmanoğlu, Peter L. Choyke, Deniz Akata, Peter A. Pinto, Baris Turkbey · 发表于：Diagnostic and Interventional Radiology · 年份：2026 · DOI：10.4274/dir.2026.264095 · 研究领域：Prostate Cancer Diagnosis and Treatment、Radiomics and Machine Learning in Medical Imaging、Prostate Cancer Treatment and Research

PURPOSE: Prostate cancer (PCa) is the second most common cancer and cause of cancer deaths among American men. Existing risk prediction methods have limited accuracy and reproducibility, resulting in difficulty in predicting treatment outcomes. We demonstrate the development and external validation of an automated multimodal artificial intelligence (AI) algorithm using biparametric magnetic resonance imaging (bpMRI) and clinical covariates for predicting biochemical recurrence (BCR) after radical prostatectomy (RP) in patients with PCa. METHODS: The development cohort included 80% of patients from center 1 (n = 240) who underwent prostate MRI prior to RP between January 2008 and December 2018, with a minimum of 2 years of follow-up after RP. The test cohort included the remaining 20% of center 1 patients (n = 71) and an external validation cohort from center 2 (n = 168). Center 2 patients included those who underwent prostate MRI and RP between January 2015 and January 2024, with a minimum of 2 years of follow-up. Clinical comparisons were made using the Cancer of the Prostate Risk Assessment Postsurgical (center 1) and International Society of Urological Pathology Gleason Grade Group (ISUP GGG) scoring systems from post-RP pathology (center 2). The models developed were as follows: clinical (M0), automated clinical (M1), radiomics (M2), and a multimodal model (M3). Clinical variables (M0) included prostate-specific antigen (PSA), age, primary Gleason, and ISUP GGG. Automated...