Research Trends and Visualization Analysis of Stereotactic Body Radiation Therapy for Hepatocellular Carcinoma from 2004 to 2025: A Bibliometric Study (Preprint)
Bibliographic record
Abstract
BACKGROUND Hepatocellular carcinoma (HCC) remains a leading cause of cancer-related mortality globally. It is often diagnosed at advanced stages or in patients unsuitable for traditional curative treatments. Stereotactic body radiation therapy (SBRT), also known as stereotactic ablative radiotherapy (SABR), has emerged as a highly effective and non-invasive local treatment, demonstrating excellent local control rates across various HCC stages. This bibliometric study aims to comprehensively analyze the research trends, hotspots, and collaborative patterns in the field of SBRT for HCC from 2004 to 2025. OBJECTIVE This bibliometric study aims to comprehensively analyze the research trends, hotspots, and collaborative patterns in the field of stereotactic body radiation therapy (SBRT) for hepatocellular carcinoma (HCC) from 2004 to 2025. METHODS Literature on SBRT for HCC published between January 1, 2004, and November 12, 2025, was retrieved from the Web of Science Core Collection (WoSCC) database. A total of 2,216 relevant publications were identified and analyzed using bibliometric software including Biblioshiny (R version 4.4.1), VOSviewer (version 1.6.17), ScimagoGraphica (version 1.0.41), CiteSpace (version 6.1. R6), and Microsoft Excel (2019 edition). The analysis covered publication output, country/region, institution, author contributions, journal distribution, co-citation patterns, and keyword trends. RESULTS A total of 2,216 publications (1,796 articles, 420 reviews) were analyzed, revealing a significant upward trend in annual publications, with a peak of over 220 in 2021. The United States and China emerged as leading countries in research output and international collaboration, with the United States also having the highest citation count. Key institutions included the University of Toronto, the University of Texas System, and Harvard University. Laura A. Dawson and Marta Scorsetti were identified as the most prolific and influential authors. The International Journal of Radiation Oncology Biology Physics was the core journal, leading in H-index and citations. Keyword analysis identified “stereotactic body radiotherapy,” “radiotherapy,” “SBRT,” and “radiofrequency ablation” (RFA) as high-frequency terms. Recent research hotspots (2023-2025) include “Y90 radioembolization,” “open-label studies,” and “motion management,” indicating a shift towards integrated and technologically advanced approaches, along with growing interest in combining SBRT with immunotherapy and targeted therapy. CONCLUSIONS The field of SBRT for HCC has experienced rapid growth over the past two decades, with the United States and China at the forefront of research. SBRT has proven effective across early-stage, recurrent, and locally advanced HCC, particularly in combination with systemic therapies. Future research should prioritize large-scale randomized controlled trials (RCTs), explore SBRT’s synergy with novel immunotherapies, optimize patient selection with biomarkers, and refine advanced delivery techniques to further enhance patient outcomes and broaden its clinical application.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.118 | 0.206 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".