Real-World Guide for Delivering Immunotherapy With Radiation Therapy

Immunoradiation therapy, the combination of radiation therapy (RT) and immunotherapy, represents a promising advancement in cancer treatment by enhancing local and systemic tumor control and improving patient response rates. Although RT has long been a cornerstone of oncologic care, its efficacy is often limited by various tumor resistance mechanisms. Immunotherapy, which leverages the immune system to combat cancer, offers a complementary means to enhance RT's effectiveness. This review aims to provide a real-world guide for optimizing immunoradiation therapy in clinical practice and to bridge the gap between research and clinical application.

This review synthesizes current literature and clinical experience related to the integration of immunotherapy and RT, focusing on four critical areas: pretreatment factors, immunotherapy sequencing, RT planning, and posttreatment follow-up. Key considerations include prior patient treatments, the timing and type of immunotherapy, radiation dosing and fractionation strategies, tumor size, organs at risk, and effective post-RT monitoring, which collectively will help tailor a precise approach. In addition, the review highlights emerging approaches that incorporate artificial intelligence into RT planning and the development of personalized immunoradiation therapy.

Evidence from recent studies demonstrates that the combination of RT and immunotherapy can enhance both local tumor control and systemic immune activation. To dissect further, the results of our review emphasize the importance of variables impacting treatment outcomes such as: prior chemotherapy, use of steroids, absolute lymphocyte counts, radiation dose and sequencing of immunotherapy in regard to RT, avoiding organs at risk, devising novel lymphocyte sparing techniques, and pulsed RT approach in cases of progression.

By addressing the key factors influencing treatment design and implementation, immunoradiation therapy can be effectively tailored to improve therapeutic outcomes. Furthermore, the integration of artificial intelligence and multidisciplinary coordination may help in automated planning, treatment precision, and advancing personalized care in clinical oncology.