Personalized predictions of neoadjuvant chemotherapy response in breast cancer using machine learning and full-field digital mammography radiomics.

ABSTRACT:

Objective

This study aimed to develop a comprehensive nomogram model by integrating clinical pathological and full-field digital mammography (FFDM) radiomic features to predict the efficacy of neoadjuvant chemotherapy (NAC) in breast cancer patients, thereby providing personalized treatment recommendations.

Methods

A retrospective analysis was conducted on the clinical and imaging data of 227 breast cancer patients from 2016 to 2024 at the Second Affiliated Hospital of Harbin Medical University. The patients were divided into a training set (n = 159) and a test set (n = 68) with a 7:3 ratio. The region of interest (ROI) was manually segmented on FFDM images, and features were extracted and gradually selected. The rad-score was calculated for each patient. Five

SUBMITTER: Ruan Y 

PROVIDER: S-EPMC12043669 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature