Surveillance for HCC After Liver Transplantation: Increased Monitoring May Yield Aggressive Treatment Options and Improved Postrecurrence Survival
Bibliographic record
Abstract
BACKGROUND: Currently, no surveillance guidelines for hepatocellular carcinoma (HCC) recurrence after liver transplantation (LT) exist. In this retrospective, multicenter study, we have investigated the role of surveillance imaging on postrecurrence outcomes. METHODS: Patients with recurrent HCC after LT from 2002 to 2016 were reviewed from 3 transplant centers (University of California San Francisco, Mayo Clinic Florida, and University of Toronto). For this study, we proposed the term cumulative exposure to surveillance (CETS) as a way to define the cumulative sum of all the protected intervals that each surveillance test provides. In our analysis, CETS has been treated as a continuous variable in months. RESULTS: Two hundred twenty-three patients from 3 centers had recurrent HCC post-LT. The median follow-up was 31.3 months, and median time to recurrence was 13.3 months. Increasing CETS was associated with improved postrecurrence survival (hazard ratio, 0.94; P < 0.01) as was treatment of recurrence with resection or ablation (hazard ratio, 0.31; P < 0.001). An receiver operating characteristic curve (area under the curve, 0.64) for CETS covariate showed that 252 days of coverage (or 3 surveillance scans) within the first 24 months provided the highest probability for aggressive postrecurrence treatment. CONCLUSIONS: In this review of 223 patients with post-LT HCC recurrence, we found that increasing CETS does lead to improved postrecurrence survival as well as a higher probability for aggressive recurrence treatment. We found that 252 days of monitoring (ie, 3 surveillance scans) in the first 24 months was associated with the ability to offer potentially curative treatment.
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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) | 0.000 | 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".