Automated detection of aggressive and indolent prostate cancer on magnetic resonance imaging.

ABSTRACT:

Purpose

While multi-parametric magnetic resonance imaging (MRI) shows great promise in assisting with prostate cancer diagnosis and localization, subtle differences in appearance between cancer and normal tissue lead to many false positive and false negative interpretations by radiologists. We sought to automatically detect aggressive cancer (Gleason pattern ≥ 4) and indolent cancer (Gleason pattern 3) on a per-pixel basis on MRI to facilitate the targeting of aggressive cancer during biopsy.

Methods

We created the Stanford Prostate Cancer Network (SPCNet), a convolutional neural network model, trained to distinguish between aggressive cancer, indolent cancer, and normal tissue on MRI. Ground truth cancer labels were obtained by registering MRI with whole-mount digital histo

SUBMITTER: Seetharaman A 

PROVIDER: S-EPMC8360053 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature