Synergizing radiotherapy (RT) with immune checkpoint inhibitors has emerged as a promising strategy for solid tumors. RT acts as a potent immunomodulator, capable of functioning as an in situ vaccine through the induction of immunogenic cell death and activation of innate immune sensing, thereby promoting DC maturation and CD8+ T cell responses. However, RT also triggers counter-regulatory immunosuppression, including PD-L1 upregulation and the recruitment of suppressive cells, providing the biological rationale for synergy. Here, we systematically review advances in radioimmunotherapy, covering immunomodulatory mechanisms, clinical optimization of dose and sequencing, and the emerging role of artificial intelligence (AI) in guiding treatment paradigms. We adopt a spatial interaction–centric perspective to synthesize current knowledge on how RT governs the DC/CD8+ T cell interaction axis across the tumor microenvironment and tumor-draining lymph nodes, aiming to chart a rational course from empirical combination toward personalized, precision radioimmunotherapy. Furthermore, we explore how AI-driven analysis of radiomics and multiomics data is being applied to predict responders and personalize treatment planning.
Lu Lu, Liufu Deng
Usage data is cumulative from August 2026 through August 2026.
| Usage | JCI | PMC |
|---|---|---|
| Text version | 709 | 0 |
| 123 | 0 | |
| Figure | 99 | 0 |
| Citation downloads | 26 | 0 |
| Totals | 957 | 0 |
| Total Views | 957 | |
Usage information is collected from two different sources: this site (JCI) and Pubmed Central (PMC). JCI information (compiled daily) shows human readership based on methods we employ to screen out robotic usage. PMC information (aggregated monthly) is also similarly screened of robotic usage.
Various methods are used to distinguish robotic usage. For example, Google automatically scans articles to add to its search index and identifies itself as robotic; other services might not clearly identify themselves as robotic, or they are new or unknown as robotic. Because this activity can be misinterpreted as human readership, data may be re-processed periodically to reflect an improved understanding of robotic activity. Because of these factors, readers should consider usage information illustrative but subject to change.