Dual-radiomics based on SHapley additive explanations for predicting hematologic toxicity in concurrent chemoradiotherapy patients.

ABSTRACT:

Background

This study investigates the application of a machine learning model that integrates radiomic features and dosiomic features to predict hematologic toxicity (HT) in patients with advanced cervical cancer undergoing concurrent chemoradiotherapy (CCRT). Two integration methods based on SHapley Additive exPlanations (SHAP) values for dual-radiomic features were compared.

Methods

Clinical information, planning CT images, and dose distribution files from 205 patients with advanced cervical cancer treated with CCRT were retrospectively collected. Patients were categorized by HT severity, with 80% of the data used for training and 20% for testing. Radiomic features and dosiomic features were extracted from the same regions of interest, and SHAP-based feature selection was

SUBMITTER: Chen L 

PROVIDER: S-EPMC12003243 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature