Liver transplantation for HCC with macrovascular invasion: A systematic review and meta-analysis of observational studies
Bibliographic record
Abstract
Background & Aims Traditional guidelines discourage liver transplantation (LT) in patients with hepatocellular carcinoma (HCC) with macrovascular invasion (MVI). However, emerging evidence suggests long-term survival is possible when LT is preceded by downstaging therapies. Pooled analysis of time-dependent risk factor effect size on overall survival (OS) is warranted to (1) determine the effect size of MVI, (2) estimate risk mitigation through downstaging and (3) design future trials. Methods MEDLINE, EMBASE, Cochrane Database of Systematic Reviews, and the Cochrane Register were systematically searched from their inception to January 24, 2023. Studies with comparative oncologic outcomes data between patients with HCC and MVI (MVI group) and HCC patients without MVI (non-MVI group) were included. Frequentist pairwise meta-analysis (random-effects model) was performed for primary outcomes of OS and recurrence-free survival (RFS) at 5 years. Only studies with adjusted effect estimates were considered for analysis. Results Ten studies were included in this systematic review and meta-analysis, contributing 15,899 patients. Seven studies included deceased-donor LT, and four studies included living donor LT. In the quantitative analysis of studies reporting adjusted effect estimates, presence of MVI was associated with lower 5y-OS (HR 2.03; 95%CI 1.60, 2.57; p -value<0.001 | I 2 =55%; p -value=0.05) and 5y-RFS (HR 2.55; 95%CI 1.69, 3.85; p -value<0.001 | I 2 =87%; p -value<0.01). When downstaging was uniformly applied, no statistically significant difference for 5y-OS was observed between the MVI group vs. non-MVI group (HR 1.55; 95%CI 0.88, 2.73; p -value=0.129 | I 2 =46%; p -value=0.17). Conclusion Effective downstaging in carefully selected HCC patients with MVI may achieve survival outcomes approaching those without MVI. Further studies are essential to validate these findings and to clarify which downstaging approaches and tumor characteristics are most likely to confer a transplant benefit. IMPACT AND IMPLICATIONS • This meta-analysis provides the first pooled hazard ratios for the effect of macrovascular invasion (MVI) on liver transplant outcomes, emphasizing the potential for downstaging therapies to mitigate poor prognosis in hepatocellular carcinoma (HCC).• The findings advocate for redefining liver transplant eligibility criteria to incorporate successful downstaging protocols, potentially expanding treatment options for advanced HCC.• These insights underline the need for standardized downstaging protocols and prospective trials to optimize patient selection and outcomes globally.
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
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gpt | no category Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Meta-analysis | high |
| grok | Meta-epidemiology (broad) Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Meta-analysis | high |
| opus | no category Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Meta-analysis | high |
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 | 0.011 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.030 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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, unvalidatedLabeled directly by 3 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".