Preliminary Results of Prospective Clinical Study Evaluating Machine Learning Software for Detecting Significant Prostate Cancer on bpMRI

Motivation: Prostate cancer (PCa) diagnosis through manual MRI interpretation is variable and prone to overdiagnosis, leading to unnecessary biopsies. PROVIZ, a radiomics-based tool, aims to reduce overdiagnosis by automating clinically significant PCa (csPCa) detection on bi-parametric MRI (bpMRI). Goal(s): This study assesses the feasibility, safety, and performance of PROVIZ in detecting csPCa, aiming to validate its clinical utility in minimizing unnecessary biopsies. Approach: In this prospective clinical study, 81 biopsy-naive men underwent bpMRI, evaluated by both radiologists and PROVIZ. Targeted and systematic biopsies confirmed results. Results: PROVIZ demonstrated feasibility, safety, and higher specificity than radiologists at the same sensitivity, with six fewer biopsy referrals. Impact: his prospective clinical study suggests that PROVIZ software can enhance diagnostic accuracy in prostate cancer care, improve clinically significant lesion targeting, and potentially reduce unnecessary biopsies, thereby minimizing overdiagnosis and improving patient outcomes in prostate cancer management.