Liver Transplantation for Unresectable Colorectal Liver Metastases: A Scoping Review on Redefining Boundaries in Transplant Oncology
Bibliographic record
Abstract
Historically, colorectal liver metastases (CRLMs) have been considered a contraindication for liver transplantation (LT), primarily due to limited organ availability and concerns about oncologic efficacy. However, emerging evidence indicates that highly selected patients with unresectable CRLM can achieve long-term survival following LT-often with outcomes superior to those obtained through conventional systemic therapies. To evaluate the evolving role of LT in this setting, we conducted a scoping review of the literature. A comprehensive search was performed across PubMed, Embase, Web of Science, Scopus, and ClinicalTrials.gov, as well as ProQuest Dissertations & Theses and Google Scholar to capture gray literature. The search included English-language articles published between January 2015 and April 2025. Eligible studies included those reporting on the application of LT for patients with unresectable CRLM. This scoping review synthesizes current evidence on patient selection criteria, overall and disease-free survival, recurrence patterns, and emerging biomarkers that may guide transplant eligibility. In addition, we explore innovations in organ utilization-including living donor LT and machine perfusion technologies-that aim to expand access while addressing ethical concerns related to organ allocation. As LT for CRLM transitions from investigational use to clinical implementation, this review outlines the key challenges and future opportunities that will shape its role in the landscape of transplant oncology.
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 | 0.006 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".