ReviewThe Journal of clinical investigation2026
Radiotherapy and immunotherapy in cancer treatment: mechanisms of clinical synergy.
Review in The Journal of clinical investigation, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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Registered trials
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