Regorafenib Efficacy After Sorafenib in Patients With Recurrent Hepatocellular Carcinoma After Liver Transplantation: A Retrospective Study
Bibliographic record
Abstract
Safety of regorafenib in hepatocellular carcinoma (HCC) recurrence after liver transplantation (LT) has been recently demonstrated. We aimed to assess the survival benefit of regorafenib compared with best supportive care (BSC) in LT patients after sorafenib discontinuation. This observational multicenter retrospective study included LT patients with HCC recurrence who discontinued first-line sorafenib. Group 1 comprised regorafenib-treated patients, whereas the control group was selected among patients treated with BSC due to unavailability of second-line options at the time of sorafenib discontinuation and who were sorafenib-tolerant progressors (group 2). Primary endpoint was overall survival (OS) of group 1 compared with group 2. Secondary endpoints were safety and OS of sequential treatment with sorafenib + regorafenib/BSC. Among 132 LT patients who discontinued sorafenib included in the study, 81 were sorafenib tolerant: 36 received regorafenib (group 1) and 45 (group 2) received BSC. Overall, 24 (67%) patients died in group 1 and 40 (89%) in group 2: the median OS was significantly longer in group 1 than in group 2 (13.1 versus 5.5 months; P < 0.01). Regorafenib treatment was an independent predictor of reduced mortality (hazard ratio, 0.37; 95% confidence interval [CI], 0.16-0.89; P = 0.02). Median treatment duration with regorafenib was 7.0 (95% CI, 5.5-8.5) months; regorafenib dose was reduced in 22 (61%) patients for adverse events and discontinued for tumor progression in 93% (n = 28). The median OS calculated from sorafenib start was 28.8 months (95% CI, 17.6-40.1) in group 1 versus 15.3 months (95% CI, 8.8-21.7) in group 2 (P < 0.01). Regorafenib is an effective second-line treatment after sorafenib in patients with HCC recurrence after LT.
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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) | 0.001 | 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) | 0.001 | 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 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".