Deep Learning Artificial Intelligence and Restriction Spectrum Imaging for Patient-level Detection of Clinically Significant Prostate Cancer on Biparametric Magnetic Resonance Imaging.

ABSTRACT:

Background and objective

Our aim was to evaluate whether combining the maximum restriction score derived from restriction spectrum imaging (RSIrs_max) with deep learning (DL) models can enhance patient-level detection of clinically significant prostate cancer (csPCa) in comparison to Prostate Imaging-Reporting and Data System (PI-RADS) or RSIrs_max alone.

Methods

A total of 1892 patients from seven institutions who underwent imaging between January 2016 and March 2024 were included on the basis of magnetic resonance imaging (MRI) findings and biopsy-confirmed prostate cancer diagnosis. Two DL architectures, 3D-DenseNet and 3D-DenseNet+RSI (incorporating RSIrs_max), were developed and trained using biparametric MRI and RSI data using a leave-one-

SUBMITTER: Song Y 

PROVIDER: S-EPMC12905774 | biostudies-literature | 2026 Mar

REPOSITORIES: biostudies-literature