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 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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.000 | 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 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".