Enhancing prediction of primary site recurrence in head and neck cancer using radiomics and uncertainty estimation

Introduction Head and neck squamous cell carcinomas (HNSCC) present a significant clinical challenge due to high recurrence rates despite advances in radiation and chemotherapy. Early detection of recurrence is critical for optimizing treatment outcomes and improving patient survival. Methods We developed two artificial intelligence (AI) pipelines—(1) machine learning models trained on radiomic and clinical data and (2) a Vision Transformer-based model directly applied to imaging data—to predict HNSCC recurrence using pre- and post-treatment PET/CT scans from a cohort of 249 patients. We incorporated Test-Time Augmentation (TTA) and Conformal Prediction to quantify prediction uncertainty and enhance model reliability. Results The machine learning models achieved an average AUC of 0.820. The vision transformer model showed moderate performance (AUC = 0.658). Uncertainty quantification enabled the exclusion of ambiguous predictions, improving accuracy among more confident cases. Discussion Our machine learning models achieved strong performance in predicting HNSCC recurrence from radiomic and clinical features. Incorporating uncertainty quantification further improved predictive performance and reliability.