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 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".