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.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".