Machine learning identifies the association between second primary malignancies and postoperative radiotherapy in young-onset breast cancer patients.

ABSTRACT:

Background

A second primary malignant tumor is one of the most important factors affecting the long-term survival of young women with breast cancer (YWBC). As one of the main treatments for breast cancer YWBC patients, postoperative radiotherapy (PORT) may increase the risk of second primary malignancy (SPM).

Methods

Machine learning components, including ridge regression, XGBoost, k-nearest neighbor, light gradient boosting machine, logistic regression, support vector machine, neural network, and random forest, were used to construct a predictive model and identify the risk factors for SPMs with data from the Surveillance, Epidemiology and End Results. Multivariate logistic regression analysis was used to assess the risk of SPM associated with PORT. The cumulative incidence

SUBMITTER: Lai Y 

PROVIDER: S-EPMC11801551 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature