ArticleThe oncologist2023
Development of a Multivariate Prognostic Model for Lenvatinib Treatment in Hepatocellular Carcinoma.
Article in The oncologist, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 8 citations in OpenAlex.
- Development and validation of a prognostic nomogram for predicting the progression risk of HCC after lenvatinib therapy.Naunyn-Schmiedeberg's archives of pharmacology · 2026Article
- A nomogram model for predicting risk factors and the outcome of skin ulcer.Annals of medicine · 2025Observational
- Outcome prediction model for patients with unresectable hepatocellular carcinoma treated with targeted therapy.Scientific reports · 2025Article
- Statistical Cure After Hepatectomy for Hepatitis B Virus-Associated Hepatocellular Carcinoma: A Risk-Stratification Model.Annals of surgical oncology · 2025Article
- Machine Learning-Based Survival Analysis for Patients Receiving Lenvatinib for Unresectable Hepatocellular Carcinoma.Journal of hepatocellular carcinoma · 2025Article
- Lenvatinib and immune-checkpoint inhibitors in hepatocellular carcinoma: mechanistic insights, clinical efficacy, and future perspectives.Journal of hematology & oncology · 2024Review
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundLenvatinib is a first-line agent for advanced hepatocellular carcinoma (HCC), but individual responses to treatment are highly heterogeneous. The aim of this study was to investigate the clinical parameters that influence the efficacy of Lenvatinib and to develop a prognostic model.
methodsWe retrospectively enrolled 333 Lenvatinib-treated patients with HCC with a median age of 57 years. Two hundred nd sixty-three of these patients had BCLC (2022) stage C. The median overall survival (mOS) time within the cohort was 12.1 months, and the median progression-free survival (mPFS) time was 4.7 months. Univariate Cox regression, best subset regression, and Lasso regression were used to screen primary variables for possible contribution to OS, multivariate Cox analysis was used to fit selected models, and the final model was selected using the maximum area under the curve (AUC) and minimum AIC. Receiver operating curves (ROC), calibration curves, and decision curve analysis were plotted to assess model performance, and 5-fold cross-validation was performed for internal validation. X-tile software was used to select the best cutoff points and to divide the study cohort into 3 different risk groups.
resultsSeven variables were included in the final model: BCLC stage, prior transarterial chemoembolization and immunotherapy history, tumor number, prognostic nutritional index, log (alpha-fetoprotein), and log (platelet-to-lymphocyte ratio). We named this final model the "multivariate prognostic model for Lenvatinib" (MPML), and a nomogram was constructed to predict the probability of survival at 6, 9, and 12 months. The MPML had good discrimination, calibration, and applicability. Cross-validation showed mean AUC values of 0.7779, 0.7738, and 0.7871 at 6, 9, and 12 months, respectively. According to nomogram points, mOS time was 21.57, 8.70, and 5.37 months in the low, medium, and high-risk groups, respectively (P < .001), and these differences were also observed in the PFS survival curve (P < .001).
conclusionsThe MPML stratified patients according to baseline clinical characteristics had a strong performance in predicting Lenvatinib efficacy and has the potential for use as an auxiliary clinical tool for individualized decision-making.
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