Recurrent Hepatocellular Carcinoma After Transplantation
Bibliographic record
Abstract
Milan and University of California at San Francisco (UCSF) criteria are used to select patients with hepatocellular carcinoma (HCC) for liver transplantation (LT). Recurrent HCC is a significant cause of death. There is no widely accepted pathological assessment strategy to predict recurrent HCC after transplantation. This study compares the pathology of patients meeting Milan and UCSF criteria and develops a pathological score and nomogram to assess the risk of recurrent HCC after transplantation. All explanted livers with HCC from our center over the 18-yr period 1985 to 2003 were assessed for multiple pathological features and relevant clinical data were recorded; multivariate analysis was performed to determine features associated with recurrent HCC. Using pathological variables that independently predicted recurrent HCC, a pathological score and nomogram were developed to determine the probability of recurrent HCC. Of 75 cases analyzed, 50 (67%) met Milan criteria, 9 (12%) met only UCSF criteria and 16 (21%) met neither criteria based on explant pathology. There were 20 cases of recurrent HCC and the mean follow-up was 8 yr. Recurrent HCC was more common (67 vs. 12%; P < 0.001) and survival was lower (15 vs. 83% at 5 yr; 15 vs. 55% at 8 yr; P < 0.001) with those who met only UCSF criteria, compared to those who met Milan criteria. Cryptogenic cirrhosis (25 vs. 5%; P = 0.015), preoperative AFP >1,000 ng/mL (20 vs. 0%; P < 0.001) and postoperative OKT3 use (40 vs. 15%; P = 0.017) were more common among patients with recurrent HCC. While microvascular invasion was the strongest pathological predictor of recurrent HCC, tumor size >or=3 cm (P = 0.004; odds ratio [OR] = 7.42), nuclear grade (P = 0.044; OR = 3.25), microsatellitosis (P = 0.020; OR = 4.82), and giant/bizarre cells (P = 0.028; OR = 4.78) also predicted recurrent HCC independently from vascular invasion. The score and nomogram stratified the risk of recurrent HCC into 3 tiers: low (<5%), intermediate (40-65%), and high (>95%). In conclusion, compared to patients meeting Milan criteria, patients who meet only UCSF criteria have a worse survival and an increased rate of recurrent HCC with long-term follow-up, as well as more frequent occurrence of adverse histopathological features, such as microvascular invasion. Application of a pathological score and nomogram could help identify patients at increased risk for tumor recurrence, who may benefit from increased surveillance or adjuvant therapy.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".