Deep learning-based artificial intelligence for prostate cancer detection at biparametric MRI.

ABSTRACT:

Purpose

To present fully automated DL-based prostate cancer detection system for prostate MRI.

Methods

MRI scans from two institutions, were used for algorithm training, validation, testing. MRI-visible lesions were contoured by an experienced radiologist. All lesions were biopsied using MRI-TRUS-guidance. Lesions masks, histopathological results were used as ground truth labels to train UNet, AH-Net architectures for prostate cancer lesion detection, segmentation. Algorithm was trained to detect any prostate cancer ≥ ISUP1. Detection sensitivity, positive predictive values, mean number of false positive lesions per patient were used as performance metrics.

Results

525 patients were included for training, validation, testing of the algorithm. Dataset was split into tr

SUBMITTER: Mehralivand S 

PROVIDER: S-EPMC10506420 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature