Stereotactic Body Radiation Therapy for Hepatocellular Carcinoma: Current Trends and Controversies
Bibliographic record
Abstract
Hepatocellular carcinoma is the fourth leading cause of cancer-related death worldwide. Depending on the extent of disease and competing comorbidities for mortality, multiple liver-directed therapy options exist for the treatment of hepatocellular carcinoma. Advancements in radiation oncology have led to the emergence of stereotactic body radiation therapy as a promising liver-directed therapy, which delivers high doses of radiation with a steep dose gradient to maximize local tumor control and minimize radiation-induced treatment toxicity. In this study, we review the current clinical data as well as the unresolved issues and controversies regarding stereotactic body radiation therapy for hepatocellular carcinoma: (1) Is there a radiation dose-response relationship with hepatocellular carcinoma? (2) What are the optimal dosimetric predictors of radiation-induced liver disease, and do they differ for patients with varying liver function? (3) How do we assess treatment response on imaging? (4) How does stereotactic body radiation therapy compare to other liver-directed therapy modalities, including proton beam therapy? Based on the current literature discussed, this review highlights future possible research and clinical directions.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".