Ultrasound-based radiomics nomogram for predicting HER2-low expression breast cancer.

ABSTRACT:

Purpose

Accurate preoperative identification of Human epidermal growth factor receptor 2 (HER2) low expression breast cancer (BC) is critical for clinical decision-making. Our aim was to use machine learning methods to develop and validate an ultrasound-based radiomics nomogram for predicting HER2-low expression in BC.

Methods

In this retrospective study, 222 patients (108 HER2-0 expression and 114 HER2-low expression) with BC were included. The enrolled patients were randomly divided into a training cohort and a test cohort with a ratio of 8:2. The tumor region of interest was manually delineated from ultrasound image, and radiomics features were subsequently extracted. The features underwent dimension reduction using the least absolute shrinkage and selection operator (LAS

SUBMITTER: Zhang X 

PROVIDER: S-EPMC11445231 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature