Liver transplantation as a treatment for cancer: comprehensive review
Bibliographic record
Abstract
BACKGROUND: Liver transplantation for cancer indications has gained momentum in recent years. This review is intended to optimize the care setting of liver transplant candidates by highlighting current indications, technical aspects and barriers with available solutions to facilitate the guidance of available strategies for healthcare professionals in specialized centres. METHODS: A review of the most recent relevant literature was conducted for all the cancer indications of liver transplantation including colorectal cancer liver metastases, hilar cholangiocarcinoma, intrahepatic cholangiocarcinoma, neuroendocrine tumours, hepatocellular carcinoma and hepatic epitheloid haemangioendothelioma. RESULTS: Transplant benefit from the best available evidence, including SECA I, SECA II, TRANSMET studies for colorectal liver metastases, various preoperative protocols for cholangiocarcinoma patients, standard, extended selection criteria for hepatocellular carcinoma and neuroendocrine tumours, are discussed. Innovative approaches to deal with organ shortages, including machine-perfused deceased grafts, living donor liver transplantation and RAPID procedures, are also explored. CONCLUSION: Cancer indications for liver transplantation are here to stay, and the selection criteria among all cancer groups are likely to evolve further with improved prognostication of tumour biology using adjuncts such as radiomics, cancer genomics, and circulating DNA and RNA status. International prospective registry-based studies could overcome the limitations of smaller patient cohorts and lack of level 1 evidence.
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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) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| 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) | 0.004 | 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".