Deep learning models for histologic grading of breast cancer and association with disease prognosis.

ABSTRACT: Histologic grading of breast cancer involves review and scoring of three well-established morphologic features: mitotic count, nuclear pleomorphism, and tubule formation. Taken together, these features form the basis of the Nottingham Grading System which is used to inform breast cancer characterization and prognosis. In this study, we develop deep learning models to perform histologic scoring of all three components using digitized hematoxylin and eosin-stained slides containing invasive breast carcinoma. We first evaluate model performance using pathologist-based reference standards for each component. To complement this typical approach to evaluation, we further evaluate the deep learning models via prognostic analyses. The individual component models perform at or above published bench

SUBMITTER: Jaroensri R 

PROVIDER: S-EPMC9530224 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature