A deep learning system for prostate cancer diagnosis and grading in whole slide images of core needle biopsies.

ABSTRACT: Gleason grading, a risk stratification method for prostate cancer, is subjective and dependent on experience and expertise of the reporting pathologist. Deep Learning (DL) systems have shown promise in enhancing the objectivity and efficiency of Gleason grading. However, DL networks exhibit domain shift and reduced performance on Whole Slide Images (WSI) from a source other than training data. We propose a DL approach for segmenting and grading epithelial tissue using a novel training methodology that learns domain agnostic features. In this retrospective study, we analyzed WSI from three cohorts of prostate cancer patients. 3741 core needle biopsies (CNBs) received from two centers were used for training. The κquad (quadratic-weighted kappa) and AUC were measured for grade group compariso

SUBMITTER: Singhal N 

PROVIDER: S-EPMC8888647 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature