Discriminatory Gleason grade group signatures of prostate cancer: An application of machine learning methods.

ABSTRACT: One of the most precise methods to detect prostate cancer is by evaluation of a stained biopsy by a pathologist under a microscope. Regions of the tissue are assessed and graded according to the observed histological pattern. However, this is not only laborious, but also relies on the experience of the pathologist and tends to suffer from the lack of reproducibility of biopsy outcomes across pathologists. As a result, computational approaches are being sought and machine learning has been gaining momentum in the prediction of the Gleason grade group. To date, machine learning literature has addressed this problem by using features from magnetic resonance imaging images, whole slide images, tissue microarrays, gene expression data, and clinical features. However, there is a gap with regards

SUBMITTER: Mokoatle M 

PROVIDER: S-EPMC9182297 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature