Predicting Post‐Radiotherapy Epigenetic Age Acceleration From Pre‐Treatment Data Using a Machine Learning Framework in Head and Neck Cancer Patients

Background Epigenetic age acceleration in head and neck cancer (HNC) patients undergoing radiotherapy has been linked to adverse treatment outcomes. Understanding a patient's likely epigenetic aging response prior to radiotherapy has the potential to inform healthcare planning and clinical decision‐making; however, current methods cannot effectively predict these changes throughout treatment, particularly without expensive assays of epigenetic alterations. Methods This study developed and validated a machine learning framework to predict EAA following radiotherapy, utilizing pre‐radiotherapy (Time 1) sociodemographic information, symptom reports, clinical measurements, and immune biomarkers. Various machine learning models were explored to forecast EAA across three post‐treatment clinic stages: Immediately post‐radiotherapy (Time 2), six months (Time 3), and 12 months post‐radiotherapy (Time 4). Results Our results demonstrate that: (1) deep learning methods, particularly TabNet, outperform conventional algorithms with an average RMSE of 4.08 (SD = 0.32); (2) predictions are most accurate immediately post‐treatment (Time 2 RMSE: 4.87); (3) baseline immune markers, including absolute eosinophil count and hemoglobin levels, are consistent predictors across timepoints; and (4) specific patient subgroups show distinct prediction accuracies (RMSE range: 1.70–4.34). Conclusion These results suggest that pre‐treatment demographic and clinical data effectively predict post‐treatment EAA trajectories without expensive epigenetic assays, enabling cost‐effective early identification of high‐risk patients for potential targeted interventions before adverse effects manifest.