An interpretable deep learning model for detecting <i>BRCA</i> pathogenic variants of breast cancer from hematoxylin and eosin-stained pathological images.

ABSTRACT:

Background

Determining the status of breast cancer susceptibility genes (BRCA) is crucial for guiding breast cancer treatment. Nevertheless, the need for BRCA genetic testing among breast cancer patients remains unmet due to high costs and limited resources. This study aimed to develop a Bi-directional Self-Attention Multiple Instance Learning (BiAMIL) algorithm to detect BRCA status from hematoxylin and eosin (H&E) pathological images.

Methods

A total of 319 histopathological slides from 254 breast cancer patients were included, comprising two dependent cohorts. Following image pre-processing, 633,484 tumor tiles from the training dataset were employed to train the self-developed deep-learning model. The performance of the network was evaluated in the i

SUBMITTER: Li Y 

PROVIDER: S-EPMC11526788 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature