Weakly-supervised deep learning models enable HER2-low prediction from H &E stained slides.

ABSTRACT:

Background

Human epidermal growth factor receptor 2 (HER2)-low breast cancer has emerged as a new subtype of tumor, for which novel antibody-drug conjugates have shown beneficial effects. Assessment of HER2 requires several immunohistochemistry tests with an additional in situ hybridization test if a case is classified as HER2 2+. Therefore, novel cost-effective methods to speed up the HER2 assessment are highly desirable.

Methods

We used a self-supervised attention-based weakly supervised method to predict HER2-low directly from 1437 histopathological images from 1351 breast cancer patients. We built six distinct models to explore the ability of classifiers to distinguish between the HER2-negative, HER2-low, and HER2-high classes in different scenarios. The attention-based

SUBMITTER: Valieris R 

PROVIDER: S-EPMC11331614 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature