Ensemble learning approach for prediction of early complications after radiotherapy for head and neck cancer using CT and MRI radiomic features.

ABSTRACT: There are different side effects in radiotherapy of head and neck cancer (HNC) including xerostomia. The present study utilizes the addition of [Formula: see text] and [Formula: see text]-weighted Magnetic Resonance (MR) radiomic image features to typical Computed Tomography (CT)-based features and radiation dose-based characteristics and incorporates the evaluation and validation of individual and ensemble classifiers for prediction of early-onset xerostomia in radiotherapy of HNC. A total of 80 patients diagnosed with HNC were evaluated prospectively. The dataset was divided into two subsets: 70% for training and validation and 30% for testing. Stratified random sampling was used to ensure that the proportion of xerostomia cases was consistent across both subsets. This approach preserved

SUBMITTER: Khajetash B 

PROVIDER: S-EPMC12022137 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature