Performance of Deep Learning and Genitourinary Radiologists in Detection of Prostate Cancer Using 3-T Multiparametric Magnetic Resonance Imaging.

ABSTRACT:

Background

Several deep learning-based techniques have been developed for prostate cancer (PCa) detection using multiparametric magnetic resonance imaging (mpMRI), but few of them have been rigorously evaluated relative to radiologists' performance or whole-mount histopathology (WMHP).

Purpose

To compare the performance of a previously proposed deep learning algorithm, FocalNet, and expert radiologists in the detection of PCa on mpMRI with WMHP as the reference.

Study type

Retrospective, single-center study.

Subjects

A total of 553 patients (development cohort: 427 patients; evaluation cohort: 126 patients) who underwent 3-T mpMRI prior to radical prostatectomy from October 2010 to February 2018.

Field strength/sequence

3-T, T2-weighted imaging and diffu

SUBMITTER: Cao R 

PROVIDER: S-EPMC8812258 | biostudies-literature | 2021 Aug

REPOSITORIES: biostudies-literature