Recurrence beyond the Milan criteria after curative‐intent resection of hepatocellular carcinoma: A novel tumor‐burden based prediction model
Bibliographic record
Abstract
BACKGROUND: Accurate prediction of recurrence patterns of hepatocellular carcinoma (HCC) may allow for prioritization of patients for resection or transplantation as well as guide post-resection surveillance strategies. METHODS: Patients who underwent curative-intent R0 resection for HCC between 2000 and 2017 were identified using a multi-institutional database. A prognostic model that incorporated HCC tumor burden score (TBS) to predict recurrence beyond the Milan criteria (MC) was developed and validated. RESULTS: Among 718 patients who underwent R0 resection for HCC, 185 (25.8%) recurred within and 110 (15.3%) beyond the MC. On multivariable analysis, AFP more than 400 ng/mL (hazard ratio [HR] = 2.26; 95% confidence interval [CI]: 1.27-4.02), lymphovascular invasion (HR = 2.00; 95% CI: 1.14-3.50), and TBS (HR = 1.08; 95% CI: 1.03-1.12) were associated with recurrence beyond the MC. A weighted TBS-based score was constructed: [0.074*TBS + 0.692*lymphovascular invasion (yes: 1, no: 0) + 0.816*AFP > 400 (yes:1, no:0)]. Patients with a low, medium, and high TBS-based risk score had a 5-year incidence of recurring beyond the MC of 16.2%, 28.6%, and 47.2%, respectively (P < .001). The predictive accuracy of the model was very good in the training (C-index: 0.761) and validation (C-index: 0.706) datasets and outperformed the previously reported clinical risk score (CRS; C-index: 0.680). CONCLUSION: A TBS-based model accurately predicted recurrence beyond MC after curative-intent resection of HCC and outperformed the CRS. Incorporating TBS allows for better risk stratification and identifies patients in need of closer surveillance.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".