Liver transplantation as a treatment for cancer: comprehensive review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it