Recurrent Hepatocellular Carcinoma After Liver Transplantation: Validation of a Pathologic Risk Score on Explanted Livers to Predict Recurrence
Bibliographic record
Abstract
BACKGROUND: Recurrence of hepatocellular carcinoma (HCC) after liver transplantation is a major cause of morbidity and mortality. To date, there is no widely accepted pathologic assessment tool to predict HCC recurrence. In 2007, we developed a pathologic risk score that stratified patients into low, intermediate, or high risk for recurrence based on explant pathology. The aim of this study was to externally validate this risk score. METHODS: We retrospectively evaluated 124 patients over a 10-year period who 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. RESULTS: Recurrence occurred in 15 patients (12%) after a mean follow-up of 25 months. Using the pathologic risk score, 10 (8%), 21 (17%), and 93 (75%) patients were stratified into high, intermediate, and low risk of recurrence, respectively. Among these risk groups, recurrence occurred in 50%, 28.5%, and 4.3% (P < .01) of patients, respectively. Using the optimal cutoff value ≤3.5, our risk score had a sensitivity of 80% and specificity of 79% with an area under the receiver operator characteristic curve of 0.8. Those with lower risk scores had higher recurrence-free survival (P < .0001). CONCLUSIONS: Our pathologic risk score accurately risks stratified patients for HCC recurrence after liver transplant. It can be used to tailor surveillance strategies for those deemed to be at elevated risk for recurrence.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".