Prognostic Prediction of Head and Neck Cancer through Radiomics: A Stacking Ensemble Approach with Machine Learning and Deep Learning Machine Learning Models

Head and neck squamous cell carcinoma (HNSCC) poses a major challenge for global healthcare due to its high rates of mortality and morbidity. While radiotherapy remains a primary treatment option, its effectiveness can vary due to tumor heterogeneity. Advanc-es in artificial intelligence (AI) have enabled the application of radiomics to enhance cancer prognosis predictions. Method: This study proposes a stacking ensemble learning approach combined with deep learning models to predict prognosis in HNSCC patients. We utilized a dataset comprising 215 CT images with contoured Gross Tumor Volume (GTV) and Planning Target Volume (PTV) from HNSCC patients. Radiomics features were extracted and analyzed using a stacking ensemble machine learning (SEML) model, while deep learning machine learning (DLML) models were used to optimize prediction performance. Result: Our results indicated that the SEML model outperformed the DLML model in predicting prognosis outcomes, achieving an accuracy of 93%, sensitivity of 100%, and specificity of 83%. No significant difference was found between PTV and GTV for prediction performance (chi-square test, p > 0.05). Conclusion: This study highlights the effectiveness of the SEML model in improving prognostic accuracy for HNSCC pa-tients, with implications for enhancing clinical decision-making and personalizing treatment strategies.