Development of metastasis and survival prediction model of luminal and non-luminal breast cancer with weakly supervised learning based on pathomics.

ABSTRACT:

Objective

Breast cancer stands as the most prevalent form of cancer among women globally. This heterogeneous disease exhibits varying clinical behaviors. The stratification of breast cancer patients into risk groups, determined by their metastasis and survival outcomes, is pivotal for tailoring personalized treatments and therapeutic interventions. The pathological sections of radical specimens encompass a diverse range of histological information pertinent to the metastasis and survival of patients. In this study, our objective is to develop a deep learning model utilizing pathological images to predict the metastasis and survival outcomes for breast cancer patients.

Methods

This study utilized pathological sections from 204 radical mastectomy specimens obtained between Jan

SUBMITTER: Liu H 

PROVIDER: S-EPMC11759606 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature