AI-enabled precision prediction and proactive management of cutaneous toxicities in cancer immunoradiotherapy...

Immunoradiotherapy has become an increasingly important strategy for the treatment of advanced malignant tumors, but its broader application is accompanied by a high incidence of cutaneous toxicities, including radiation dermatitis and immune-related skin adverse events. These toxicities often emerge early during treatment, exhibit substantial inter-patient heterogeneity, and can compromise treatment continuity and patient quality of life. Conventional management remains largely reactive and grading-based, offering limited capacity for individualized risk assessment or early intervention. Recent advances highlight that cutaneous toxicity in immunoradiotherapy arises from the convergence of radiation-induced tissue injury and immune checkpoint blockade-driven immune amplification, involving interconnected pathways such as DNA damage-associated danger signaling, innate immune activation, cytokine amplification, and dysregulated T-cell effector responses. This biological complexity limits the predictive utility of single-factor or mechanism-isolated approaches, underscoring the need for integrative, data-driven strategies. In this mini-review, we synthesize emerging AI-enabled approaches for precision prediction and management of cutaneous toxicities in immunoradiotherapy. We focus on how clinicodosimetric variables, spatial dose topology, imaging- and radiomics-derived tissue susceptibility, and accessible immune-inflammatory surrogates can be integrated into pathway-informed predictive models. We further discuss translational frameworks that embed prediction into clinical workflows, enabling plan-aware exposure mitigation, proactive supportive care stratification, and dynamic on-treatment risk updating. Collectively, these advances position cutaneous toxicity as a tractable and clinically meaningful endpoint for precision management in immunoradiotherapy, aligned with the goals of data-driven oncology.