Comparative Outcome Analysis of Lenvatinib Versus Sorafenib for Recurrence of Hepatocellular Carcinoma After Liver Transplantation
Bibliographic record
Abstract
BACKGROUND: Hepatocellular carcinoma (HCC) recurs after liver transplantation (LT) in ~17% of patients. We aimed to retrospectively compare the outcomes of patients treated with different tyrosine kinase inhibitors (TKIs) for recurrent HCC post-LT. METHODS: Patients with recurrent HCC post-LT between 2006 and 2019 were included. The impact of sorafenib and lenvatinib treatment for recurrent disease was assessed using survival analysis with an a priori multivariable Cox regression (alpha-fetoprotein [AFP] at recurrence, recurrence lesion diameter, single-site versus multisite metastases). RESULTS: Seven hundred fifty-four patients underwent LT for HCC, of whom 120 (15.9%) developed recurrence. Of these patients, 56 received TKIs: sorafenib (n = 42) or lenvatinib (n = 14). The median age at LT was 60.8 y (interquartile range, 54.0-66.2); 52 (93%) were men and 26 (46%) were within Milan criteria at listing. Baseline characteristics at recurrence were comparable between the 2 groups, including largest tumor diameter ( P = 0.15), receipt of local therapies before TKI ( P = 0.33), and single-site recurrence ( P = 0.75), and time from interventional treatment to start of TKI ( P = 0.44). The AFP at recurrence was higher in the sorafenib group (95.0 versus 3.0 µg/L, P < 0.001). The median overall survival (OS) after initiation of TKI treatment was longer in the lenvatinib group (15.0 mo [95% confidence interval [CI], 11.5-31.5] versus 7.8 mo [95% CI, 4.0-15.4]; P = 0.02) with a 2.3-fold a priori adjusted effect on OS (adjusted hazard ratio 2.32 [95% CI, 1.03-5.20], P = 0.04). CONCLUSIONS: Our findings suggest lenvatinib is a valuable treatment option for 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 | 0.001 | 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".