Tailored Prostate MRI Screening: A Deep Learning Classifier for Intelligent Scanning

Motivation: Multiparametric prostate MRI is lengthy and costly, presenting a challenge for widespread implementation. Goal(s): To develop an automated triage protocol using a deep learning classifier to discern, based on an abbreviated biparametric MR examination, between high-risk patients who would benefit from additional sequences and low-risk patients who would not. Approach: A double-branched ResNet50 with 3D convolutions was trained on biparametric scans to predict the presence of clinically significant prostate cancer. Results: The classifier achieved a sensitivity of 0.93 with 88% negative predictive value, indicating potential to reduce comprehensive MRI exams for those without clinically significant disease by 40%. Impact: Our triage protocol has the potential to streamline prostate cancer screening by reducing the number of full mpMRI exams, thereby lowering healthcare costs. The classifier could pave the way for personalized, risk-adaptive screening protocols, allowing more precise and resource-efficient diagnostics.