Machine learning predictor to investigate treatment modalities and overall survival in HER2+ patients with early-stage breast cancer.

ABSTRACT:

Purpose

This study aimed to explore the impact of treatment modalities on the survival outcomes of HER2-positive patients with early-stage invasive ductal breast cancer.

Methods

Hierarchical clustering analysis was used to identify distinct subgroups based on treatment modalities. Comparative analysis between the clusters identified significant treatment-related variables. Cox regression analysis was performed to construct a survival prediction model and nomogram incorporating these variables. Random Survival Forest (RSF) and SHapley Additive exPlanations (SHAP) analysis were employed for further validation and interpretation.

Results

A total of 9569 patients with early-stage HER2+ invasive ductal breast cancer were included, and five treatment-related clusters were i

SUBMITTER: Wang K 

PROVIDER: S-EPMC12617624 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature