Image analysis-based identification of high risk ER-positive, HER2-negative breast cancers.

ABSTRACT:

Background

Breast cancer subtypes Luminal A and Luminal B are classified by the expression of PAM50 genes and may benefit from different treatment strategies. Machine learning models based on H&E images may contain features associated with subtype, allowing early identification of tumors with higher risk of recurrence.

Methods

H&E images (n = 630 ER+/HER2-breast cancers) were pixel-level segmented into epithelium and stroma. Convolutional neural network and multiple instance learning were used to extract image features from original and segmented images. Patient-level classification models were trained to discriminate Luminal A versus B image features in tenfold cross-validation, with or without grade adjustment. The best-performing visual classifier was incorporated into en

SUBMITTER: Lee DN 

PROVIDER: S-EPMC11616316 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature