Automated Patient-level Prostate Cancer Detection with Quantitative Diffusion Magnetic Resonance Imaging

Background: Multiparametric magnetic resonance imaging (mpMRI) improves detection of clinically significant prostate cancer (csPCa), but the subjective Prostate Imaging Reporting and Data System (PI-RADS) system and quantitative apparent diffusion coefficient (ADC) are inconsistent. Restriction spectrum imaging (RSI) is an advanced diffusion-weighted MRI technique that yields a quantitative imaging biomarker for csPCa called the RSI restriction score (RSIrs). Objective: To evaluate RSIrs for automated patient-level detection of csPCa. Design, setting, and participants: We retrospectively studied all patients (n = 151) who underwent 3 T mpMRI and RSI (a 2-min sequence on a clinical scanner) for suspected prostate cancer at University of California San Diego during 2017- 2019 and had prostate biopsy within 180 d of MRI. Intervention: We calculated the maximum RSIrs and minimum ADC within the pros-tate, and obtained PI-RADS v2.1 from medical records. Outcome measurements and statistical analysis: We compared the performance of RSIrs, ADC, and PI-RADS for the detection of csPCa (grade group >2) on the best available histopathology (biopsy or prostatectomy) using the area under the curve (AUC) with two-tailed a = 0.05. We also explored whether the combination of PI-RADS and RSIrs might be superior to PI-RADS alone and performed subset anal-yses within the peripheral and transition zones.