# Synergistic effects of radiotherapy and immunotherapy: improving oncological outcomes

## TL;DR

This review explores how combining radiotherapy and immunotherapy can improve cancer treatment by understanding their synergistic effects and optimizing their use.

## Contribution

The paper introduces the dose–immune window hypothesis and highlights AI's role in optimizing RT–immunotherapy combinations.

## Key findings

- Radiotherapy and immunotherapy have distinct but complementary mechanisms in cancer treatment.
- The dose–immune window hypothesis explains how radiation doses can modulate immune responses for synergy with immunotherapy.
- Artificial intelligence is advancing treatment planning and patient stratification in RT–immunotherapy combinations.

## Abstract

Radiotherapy (RT) and immunotherapy, which are cornerstone modalities in the realm of oncology, involve distinct mechanistic pathways and possess unique therapeutic potential. RT achieves localized tumor control by inducing DNA damage and disrupting the tumor microenvironment (TME), whereas immunotherapy—particularly immune checkpoint inhibitors (ICIs)—reactivates dormant antitumor immune responses to exert systemic effects. Across randomized evaluations, evidence for RT–immunotherapy superiority over standard regimens remains inconsistent, with multiple studies failing to show improvement in primary survival endpoints. This result highlights the need for the refined optimization of combinatorial strategies. In this review, we summarize the underlying mechanisms of RT–immunotherapy synergy and actionable strategies to increase therapeutic efficacy. Notably, we elaborate on the dose–immune window hypothesis, which delineates how distinct radiation doses modulate immune responses to achieve synergy with immunotherapy, and we highlight recent advances in artificial intelligence (AI) for optimizing treatment planning, patient stratification, and toxicity predictions. Overall, this review underscores the potential of RT–immunotherapy combinations and provides a framework for precision-based optimization, aiming to guide clinical practice and inspire future research in improving oncological outcomes.