Current Understanding of Ablative Radiation Therapy in Hepatocellular Carcinoma
Bibliographic record
Abstract
The role of ablative stereotactic body radiotherapy (SBRT) in hepatocellular carcinoma (HCC) has been evolving over the last few decades. SBRT has mostly been used in early stages of HCC, including few (≤ 3 in number) tumors, small tumours (< 3 cm in size), as well as larger tumours which are ineligible for other ablative modalities, mostly without vascular invasion. In early stage HCC, SBRT is used as a definitive treatment with curative intent or with intent to bridge to liver transplant. Retrospective and prospective institutional series document a high rate of local control (68-95% at 3 years) following SBRT. This coupled with a low risk of toxicity makes this non-invasive ablative treatment an attractive option for patients who are ineligible for other ablative treatments. Small randomized studies of ablative radiation have also shown non-inferiority of radiation as compared to radiofrequency ablation (RFA). Currently, SBRT is widely available as a safe and effective liver directed therapy, although there is a need for more studies providing higher level evidence. This review gives a brief overview of SBRT and the evidence for its use in HCC patients with ablative intent.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".