Multimodal recurrence risk prediction model for HR+/HER2- early breast cancer following adjuvant chemo-endocrine therapy: integrating pathology image and clinicalpathological features.

ABSTRACT:

Background

In HR+/HER2- early breast cancer (EBC) patients, approximately one-third of stage II and 50% of stage III patients experience recurrence, with poor outcomes after recurrence. Given that these patients commonly undergo adjuvant chemo-endocrine therapy (C-ET), accurately predicting the recurrence risk is crucial for optimizing treatment strategies and improving patient outcomes.

Methods

We collected postoperative histopathological slides from 1095 HR+/HER2- EBC who received C-ET and were followed for more than five years at West China Hospital, Sichuan University. Two deep learning pipelines were developed and validated: ACMIL-based and CLAM-based. Both pipelines, designed to predict recurrence risk post-treatment, were based on pretrained feature encoders and multi

SUBMITTER: Wu X 

PROVIDER: S-EPMC11951786 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature