Predicting Post-Radiotherapy Epigenetic Age Acceleration From Pre-Treatment Data Using a Machine Learning Framework
Runze Yan1, Guanlin Dai1, Yufen Lin1,2
1Nell Hodgson Woodruff School of Nursing, Emory University, Atlanta, USA.
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.