Prognostic prediction of head and neck cancer through radiomics: a stacking ensemble approach with machine learning and deep learning models

Abstract

Aim: This study aimed to develop and evaluate a stacking ensemble machine learning (SEML) model that integrates deep learning (DL) algorithms to improve the accuracy of prognostic predictions for patients with head and neck squamous cell carcinoma (HNSCC). Methods: A cohort of 215 HNSCC patients’ CT images, featuring gross tumor volume (GTV) and planning target volume (PTV) contours, was analyzed. Radiomics features were extracted and converted into quantitative data. These features were then used to train and compare a novel SEML model against standard DL algorithms to predict patient prognosis. Results: The proposed SEML model demonstrated superior predictive performance compared to the DL model, achieving 93% accuracy, 100% sensitivity, and 83% specificity. Statistical analysis using the chi-square test indicated no substantial difference in prediction performance between features derived from GTV and PTV contours (p > 0.05). Conclusions: The SEML model effectively enhances the prognostic prediction accuracy for HNSCC based on radiomic features. This approach shows significant potential to inform clinical decision-making and support the development of customized treatment strategies for improved patient care.

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