A radiomic model leveraging conventional and Hessian matrix-based radiomic features from DCE-MRI for predicting efficacy of neoadjuvant chemotherapy in patients with HER2-low breast cancer.

ABSTRACT:

Purpose

This study aimed to develop a predictive model for assessing the efficacy of neoadjuvant chemotherapy (NAC) in patients with Human Epidermal Growth Factor Receptor 2 (HER2)-low breast cancer, integrating clinical factors and radiomic features.

Method

We retrospectively analyzed data from patients with HER2-low breast cancer who underwent NAC. Radiomic features were extracted from pre-treatment imaging, including wavelet-based and Hessian matrix-based features. Various machine learning models were constructed using radiomic and clinicopathological features. The Shapley additive explanations (SHAP) analysis was used to assess feature contributions. Model performance was evaluated based on the area under the receiver operating characteristic curve (AUC), accuracy, sensi

SUBMITTER: Zou Y 

PROVIDER: S-EPMC12835305 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature