Outcome Prediction in Older Adults With Head and Neck Cancer Undergoing Chemoradiation

Importance Older adults with head and neck squamous cell carcinoma (HNSCC) are underrepresented in clinical trials, limiting evidence-based treatment decisions. Artificial neural networks (ANNs) have demonstrated the ability to personalize treatment recommendations using patient-specific characteristics. Objective To develop and externally validate ANNs for overall survival (OS) and progression-free survival (PFS) in older adults with HNSCC undergoing definitive chemoradiation. Design, Setting, and Participants This international cohort study included retrospective clinical data from 19 academic cancer centers across Germany, Switzerland, Czech Republic, Cyprus, and the US from the SENIOR registry. ANNs were developed and validated using data from patients 65 years and older with locoregionally advanced HNSCC treated with definitive chemoradiation. Exclusion criteria included induction or adjuvant chemotherapy, history of head and neck cancer, and metastatic disease at treatment initiation. Data were collected from January 2021 to December 2023, and data were analyzed from December 2023 to April 2025. Exposures All patients received definitive radiotherapy with concurrent systemic therapy between 2005 and 2019. Main Outcomes and Measures OS and PFS were predicted using 2 separate ANN models. Patients were classified as high or low risk based on median prediction thresholds. Model performance was assessed with receiver operating characteristic (ROC) area under the curve (AUC) and precision recall AUC. Model explainability was assessed with Shapley additive explanations values. Results Of 898 patients included in the OS analysis (738 in training cohort and 160 in testing cohort), 665 (74.1%) were male, and the median (IQR) age was 71 (68-76) years. Of 945 included in the PFS analysis (770 in training cohort and 175 in testing cohort), 696 (73.7%) were male, and the median (IQR) age was 71 (68-76) years. The OS ANN stratified patients into high-risk and low-risk groups with significantly different survival, achieving an ROC-AUC of 0.68 (95% CI, 0.60-0.76). The PFS ANN showed similar discrimination, with an ROC-AUC of 0.64 (95% CI, 0.56-0.72). Human papillomavirus status, kidney function (estimated glomerular filtration rate), Eastern Cooperative Oncology Group Performance Status score, and nodal classification were among the most predictive features. Conclusions and Relevance In this study, ANN-based models using routine clinical data effectively stratified older adults with HNSCC into prognostic groups. Integration of ANNs into clinical workflows could support personalized treatment decisions for this vulnerable population.