Stereotactic Radiotherapy of the Liver: A Bridge to Transplantation
Bibliographic record
Abstract
Patients with hepatocellular carcinoma (HCC) have limited curative therapeutic options. In North America, liver transplantation is one of the most commonly used curative therapies. Many potential transplant patients will be treated with another therapeutic modality to prevent local disease progression while awaiting organ donation. We present the case of 60-years old male diagnosed with HCC and awaiting liver transplantation. Prior to registration on the transplant list, the patient had a significant increase of his serum alpha-fetoprotein level. Due to his vascular anatomy and tumor location, he was not a candidate for more standard local ablative therapies. He was thus offered stereotactic radiotherapy as a bridge to liver transplantation. He received 50 Gy in 5 fractions using respiratory gating. Following this, he had a complete radiological and serological response without worsening of his baseline Child-Pugh class C cirrhosis. Following transplant, 13 months later, pathological examination of the liver explant revealed only scarring at the site of radiation. This case illustrated the fact that hepatic stereotactic radiotherapy is a promising and safe treatment for patients with HCC. In selected patients, it can be a bridge to transplantation and, on its own, has the potential to induce complete pathological response in non-surgical candidates.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".