Liver Transplantation for Advanced Hepatocellular Carcinoma Using Poor Tumor Differentiation on Biopsy as an Exclusion Criterion
Bibliographic record
Abstract
In Brief Background: Liberal acceptance criteria are used when offering liver transplantation (LTx) for treatment of hepatocellular carcinoma (HCC) at our center. This provides a unique opportunity to assess outcomes in a large North American series of patients with advanced tumors. Objective: We hypothesized that acceptable survival rates can be achieved with LTx for any size or number of HCC provided that (a) imaging studies ruled out vascular invasion; (b) the HCC was confined to the liver; and (c) the HCC was not poorly differentiated on biopsy. Methods: Survival, based on pretransplant imaging staging, was compared between 189 Milan Criteria (M) and 105 beyond Milan Criteria (M+) HCC patients who received an LTx between 1996 and 2008. Results: Imaging understaged 30% of the M group and overstaged 23% of the M+ group. There was no difference in the 5-year overall survival in the M (72%) and M+ (70%) groups or 5-year disease-free survival in the M (70%) and M+ (66%) groups. The introduction of a protocol for a biopsy to exclude patients with poorly differentiated tumors and use of aggressive bridging therapy improved overall survival in the M+ group (P = 0.034). Serum alpha-fetoprotein more than 400 at LTx was associated with poorer disease-free survival (hazard ratio: 2.3; P = 0.031). Conclusions: Cross-sectional imaging did not reliably stage patients with HCC for LTx. A protocol using a biopsy to exclude poorly differentiated tumors and aggressive bridging therapy achieved excellent survival rates with LTx for otherwise incurable advanced HCC, irrespective of tumor size and number. We examined the survival of a large cohort of patients transplanted for advanced hepatocellular carcinoma. Macrovascular invasion was the only criteria used to exclude patients from transplantation; tumor number and size were not considered. The 5-year overall survival was excellent in both Milan (72%) and beyond Milan Criteria (70%) groups.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".