Abstract / Summary
Radiotherapy for head and neck cancer must balance disease control against normal-tissue injury. Artificial intelligence (AI), multi-omics, and multimodal analysis offer complementary ways to characterize tumor and host heterogeneity, but their relevance to treatment decisions depends on how measurements, outcomes, and validation are linked. This narrative review examines these approaches across head and neck cancers, with evidence concentrated in head and neck squamous cell carcinoma and nasopharyngeal carcinoma. Literature was identified through Web of Science and PubMed searches with targeted supplementary searches and reference-list screening, and selected for relevance to radiotherapy biology, clinical outcomes, and methodological or clinical validation. We distinguish joint molecular or multimodal inputs from parallel analyses and post hoc biological annotation, and assess findings according to population, treatment setting, sampling time, and analytical unit. Genomic, proteogenomic, single-cell, and spatial studies identify candidate phenotypes involving deoxyribonucleic acid (DNA) repair, viral regulation, immunity, and metabolism. Clinical studies examine response, recurrence and survival stratification, molecular surveillance, and toxicity prediction. However, associations with outcomes under observed treatment do not establish differential treatment benefit, and improved discrimination does not demonstrate that model-guided care improves patient outcomes. Small patient cohorts, incomplete assay availability, potential data leakage, and limited independent validation further constrain interpretation. Translation requires reproducible measurements, patient-level validation of complete analytical pipelines, calibrated risk estimates, and evaluation within the intended treatment context. Prospective evaluation should prioritize predefined model-guided strategies and assess disease control, patient-relevant harm, and practical implementation together.