A94 RECURRENT HEPATOCELLULAR CARCINOMA: EVALUATION OF 8 POST-TRANSPLANT SCORING SYSTEMS
Bibliographic record
Abstract
Abstract Background Recurrence of hepatocellular carcinoma (HCC) after liver transplantation is a major cause of morbidity and mortality. It is well known that there is a discordance between pre-transplant imaging and post-transplant pathology that affect risk of recurrence. Several risk assessment tools have been developed, although to date, there is no widely accepted tool to predict HCC recurrence. Aims The aim of the current study is to determine which pathologic risk assessment score has the best predicative ability. Methods We retrospectively evaluated 152 patients over a twelve-year period that underwent liver transplantation for HCC. Using explanted pathology reports, each patient was stratified according to the pathologic risk score and followed over time for HCC recurrence. We evaluated eight pathologic risk scores and determined predictive ability by assessing the area under the receiver operating characteristic curve (AUROC). Results Out of 152 consecutive liver transplants for HCC, recurrence occurred in 21 patients (14%) with a mean follow-up of 59.5 months. 54% of patients were within Milan criteria prior to transplant. According to explant pathology, microvascular invasion was seen in 16% of patients, with majority of the tumors being moderately differentiated (48%), tumor size ≥ 3cm (52%), and 26% of tumors in both lobes of the liver. Preliminary data suggests that the Parfitt et. al score has the best predictive ability, with 60% of recurrence occurring in those considered high-risk. Further assessment via AUROC will be required to confirm the preliminary data. Conclusions Preliminary data suggests the Parfitt et al. score may have the best predictive ability to detect recurrence. This risk assessment tool can help tailor a surveillance strategy for early detection or early adjuvant therapy to improve long-term survival. Funding Agencies None
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".