Development and External Validation of a Machine Learning Model to Predict Pathological Complete Response After Neoadjuvant Chemotherapy in Breast Cancer.

ABSTRACT:

Purpose

Several predictive models have been developed to predict the pathological complete response (pCR) after neoadjuvant chemotherapy (NAC); however, few are broadly applicable owing to radiologic complexity and institution-specific clinical variables, and none have been externally validated. This study aimed to develop and externally validate a machine learning model that predicts pCR after NAC in patients with breast cancer using routinely collected clinical and demographic variables.

Methods

The electronic medical records of patients with advanced breast cancer who underwent NAC before surgical resection between January 2017 and December 2020 were reviewed. Patient data from Seoul National University Bundang Hospital were divided into training and internal validation c

SUBMITTER: Jung JJ 

PROVIDER: S-EPMC10475713 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature