Machine learning model for predicting epidermal growth factor receptor expression status in breast cancer using ultrasound radiomics.

ABSTRACT:

Background/objectives

The epidermal growth factor receptor (EGFR) is a clinically important target, as its expression in patients with breast cancer influences both overall and disease-free survival. Current methods for assessing EGFR expression status in a patient are invasive. Therefore, in this study, we developed a machine learning-based approach utilizing ultrasound radiomics to non-invasively predict EGFR expression status in patients with breast cancer.

Methods

Radiomic features were extracted from grayscale and wavelet-transformed ultrasound images of 321 patients. The dataset was randomly split into training (n = 225) and test (n = 96) sets at a 7:3 ratio with stratified sampling to preserve the EGFR+/- ratio. Key predictors were identified using a multi-step proced

SUBMITTER: Xu Z 

PROVIDER: S-EPMC12575132 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature