Predicting pathologic complete response to neoadjuvant chemotherapy in breast cancer using a machine learning approach.

ABSTRACT:

Background

For patients with breast cancer undergoing neoadjuvant chemotherapy (NACT), most of the existing prediction models of pathologic complete response (pCR) using clinicopathological features were based on standard statistical models like logistic regression, while models based on machine learning mostly utilized imaging data and/or gene expression data. This study aims to develop a robust and accessible machine learning model to predict pCR using clinicopathological features alone, which can be used to facilitate clinical decision-making in diverse settings.

Methods

The model was developed and validated within the National Cancer Data Base (NCDB, 2018-2020) and an external cohort at the University of Chicago (2010-2020). We compared logistic regression and machine le

SUBMITTER: Zhao F 

PROVIDER: S-EPMC11520773 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature