Assessing the RETREAT Score for Hepatocellular Carcinoma Patients in a Liver Transplant Program Guided by Total Tumour Volume Criteria
Bibliographic record
Abstract
Purpose: Liver transplantation (LT) is a curative option for hepatocellular carcinoma (HCC), but optimal candidate selection remains a challenge. The Total Tumour Volume (TTV) criteria allow for broader eligibility compared to the more commonly applied Milan criteria. The Risk Estimation of Tumour Recurrence After Transplant (RETREAT) score was developed within Milan-based programs to estimate post-LT recurrence risk; however, its performance in TTV-based LT settings is not well established. This study aimed to assess the predictive value of the RETREAT score in a TTV-based LT program. Methods: A retrospective cohort analysis was performed on 302 patients who underwent LT for HCC at the University of Alberta. Data collected included patient demographics, tumour characteristics, alpha-fetoprotein (AFP) levels, microvascular invasion status, and recurrence outcomes. RETREAT scores were calculated based on tumour size, number of tumours at explant, AFP levels, and presence of microvascular invasion. Statistical analyses comprised Kaplan–Meier survival estimates and discrimination testing using the C-statistic. Results: Among the 302 patients included, the median follow-up was 6.3 years, during which 37 patients (12.2%) experienced HCC recurrence. Recurrence rates at 1, 2, 3, and 5 years were 1.6%, 6%, 8%, and 13%, respectively. Recurrence rates by RETREAT score were as follows: 1% (score 0), 4% (score 1), 19% (score 2), 12% (score 3), 28% (score 4), 67% (score 5), 28% (score 6), and\n100% (score 7). The C-statistic for recurrence prediction was 0.786 for RETREAT, compared with 0.660 for Milan criteria and 0.719 for TTV. Conclusion: The RETREAT score demonstrates strong predictive accuracy for recurrence in a TTV-based LT program, supporting its value in identifying patients at higher risk of post-LT recurrence.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".