ReviewClinical and translational radiation oncology2026
Evaluating the efficacy and immunological impact of combined ICIs and SBRT in HCC: A narrative literature review.
Review in Clinical and translational radiation oncology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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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.
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Who cites it
1 citing paper in PubMed.
Corrections and comments
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Authors and funding
20 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Hepatocellular carcinoma (HCC) represents one of the leading contributors to cancer-related deaths, with the majority of patients diagnosed at stages where curative treatment is no longer possible. Combining stereotactic body radiotherapy (SBRT) with immune checkpoint inhibition (ICI) has gained increasing attention as a therapeutic approach. Beyond its ability to provide high local tumor control (LC), SBRT can provoke immunogenic tumor cell death, promote antigen release and presentation, and modulate the tumor microenvironment in ways that enhance systemic antitumor immunity. In this narrative review, we outline the scientific rationale for integrating SBRT with ICIs, discuss mechanistic and translational findings and summarize results from key clinical trials. The currently available data indicate a synergistic interaction, most notably reflected in improved survival and response rates. Nevertheless, variability in dose and fractionation schedules, treatment sequencing, and patient characteristics complicates interpretation. Well-designed prospective studies are needed to establish optimal protocols and identify predictive biomarkers to guide patient selection.
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