Classification of breast cancer molecular subtypes based on contrast-enhanced ultrasound and superb microvascular imaging using machine learning approach.

ABSTRACT:

Background

Varying patterns of angiogenesis are observed across molecular subtypes of breast cancer (BC). This study aimed to develop and validate machine learning (ML) models for identifying molecular subtypes of BC using contrast-enhanced ultrasound (CEUS) and superb microvascular imaging (SMI).

Methods

In this prospective study, 191 BC patients with 193 lesions were enrolled. Clinical data, CEUS parameters, and SMI features were collected; recursive feature elimination was applied for feature selection. Random forest (RF), support vector machine (SVM), and logistic regression (LR) were trained to distinguish molecular subtypes, and their diagnostic performances were compared. Model interpretability was achieved using SHapley Additive exPlanations (SHAP).

Results

BC

SUBMITTER: Chen Q 

PROVIDER: S-EPMC12552557 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature