Three‐dimensional tumor volume and serum alpha‐fetoprotein are predictors of hepatocellular carcinoma recurrence after liver transplantation: refined selection criteria
Bibliographic record
Abstract
Total tumor volume (TTV), as a better predictor of hepatocellular carcinoma (HCC) recurrence after liver transplant, has been explored by our center. Some tumors are not typically spherical but rather ellipsoid or spheroid, and calculating their TTV based on one dimension only may overestimate their volume and exclude them from candidacy for transplantation. Our aim was to study the actual tumor volume (ATV) calculated using the ellipsoid formula and assess its impact on recurrence. HCC patients transplanted between 1990 and 2010 at University of Alberta Hospital were analyzed. Tumor volumes were calculated using both formulas: [(4/3) πr(3)] (r = max. radius) and [(4/3) πabc] (a, b, c = the 3 radiuses). A total of 115 patients were included with a mean follow-up of 4.99 ± 4.23 yr. Five-yr recurrence-free survival was 79.8%. Univariate analysis for predictors of recurrence included: maximum tumor diameter, ATV, TTV, and alpha-fetoprotein (AFP) ≥ 400 ng/mL. Multivariate analysis showed that ATV and AFP ≥ 400 ng/mL were the only predictors of recurrence. Combining both variables provides better predication of recurrence with accuracy that exceeds 80%. Three-dimensional calculation of tumor volume is of critical importance for the group of patients with ellipsoid tumors where volumes are overestimated with the spherical formula and could lead to inappropriate exclusion from transplant.
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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 | 0.000 |
| 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".