Prediction of Hepatocellular Carcinoma Recurrence Beyond Milan Criteria After Resection
Bibliographic record
Abstract
OBJECTIVE: This study aims to validate a previously reported recurrence clinical risk score (CRS). SUMMARY OF BACKGROUND DATA: Salvage transplantation after hepatocellular carcinoma (HCC) resection is limited to patients who recur within Milan criteria (MC). Predicting recurrence patterns may guide treatment recommendations. METHODS: An international, multicenter cohort of R0 resected HCC patients were categorized by MC status at presentation. CRS was calculated by assigning 1 point each for initial disease beyond MC, multinodularity, and microvascular invasion. Recurrence incidences were estimated using competing risks methodology, and conditional recurrence probabilities were estimated using the Bayes theorem. RESULTS: From 1992 to 2015, 1023 patients were identified, of whom 613 (60%) recurred at a median follow-up of 50 months. CRS was well validated in that all 3 factors remained independent predictors of recurrence beyond MC (hazard ratio 1.5-2.1, all P < 0.001) and accurately stratified recurrence risk beyond MC, ranging from 19% (CRS 0) to 67% (CRS 3) at 5 years. Among patients with CRS 0, no other factors were significantly associated with recurrence beyond MC. The majority recurred within 2 years. After 2 years of recurrence-free survival, the cumulative risk of recurrence beyond MC within the next 5 years for all patients was 14%. This risk was 12% for patients with initial disease within MC and 17% for patients with initial disease beyond MC. CONCLUSIONS: CRS accurately predicted HCC recurrence beyond MC in this international validation. Although the risk of recurrence beyond MC decreased over time, it never reached zero.
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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 | 0.002 | 0.007 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".