Abstract
In modern radiation therapy for head and neck cancers, the treatment related toxicities remain a significant clinical challenge. This review critically evaluates the evolution of data-driven approaches in predicting patient outcomes in head and neck cancer patients treated with radiation therapy. Three transformative methodological advances are reviewed: radiomics, AI-based algorithms, and causal inference frameworks. The integration of linear energy transfer in patient outcomes study, which has uncovered critical mechanisms behind unexpected toxicity, was also introduced for proton therapy. While radiomics has transformed medical image analysis through comprehensive quantitative characterization, AI models have demonstrated markedly superior predictive capabilities over traditional approaches, offering promising avenues for personalized radiation therapy with reduced toxicity profiles. However, the field faces significant challenges in translating statistical correlations from real-world data into interventional clinical insights. We highlight how causal inference methods can bridge this gap by providing a rigorous framework for identifying treatment effects. Looking ahead, we envision that combining these complementary approaches, especially the interventional prediction models, will enable more personalized treatment strategies, ultimately improving both tumor control and quality of life for head and neck cancer patients treated with radiation therapy.