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Development of a Multivariate Prognostic Model for Lenvatinib Treatment in Hepatocellular Carcinoma
BACKGROUND: Lenvatinib 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. METH...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10546830/ https://www.ncbi.nlm.nih.gov/pubmed/37105140 http://dx.doi.org/10.1093/oncolo/oyad107 |
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author | Li, Xiaomi Ding, Xiaoyan Liu, Mei Wang, Jingyan Li, Wei Chen, Jinglong |
author_facet | Li, Xiaomi Ding, Xiaoyan Liu, Mei Wang, Jingyan Li, Wei Chen, Jinglong |
author_sort | Li, Xiaomi |
collection | PubMed |
description | BACKGROUND: Lenvatinib 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. METHODS: We 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. RESULTS: Seven 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). CONCLUSIONS: The 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. |
format | Online Article Text |
id | pubmed-10546830 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-105468302023-10-04 Development of a Multivariate Prognostic Model for Lenvatinib Treatment in Hepatocellular Carcinoma Li, Xiaomi Ding, Xiaoyan Liu, Mei Wang, Jingyan Li, Wei Chen, Jinglong Oncologist Hepatobiliary BACKGROUND: Lenvatinib 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. METHODS: We 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. RESULTS: Seven 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). CONCLUSIONS: The 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. Oxford University Press 2023-04-27 /pmc/articles/PMC10546830/ /pubmed/37105140 http://dx.doi.org/10.1093/oncolo/oyad107 Text en © The Author(s) 2023. Published by Oxford University Press. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Hepatobiliary Li, Xiaomi Ding, Xiaoyan Liu, Mei Wang, Jingyan Li, Wei Chen, Jinglong Development of a Multivariate Prognostic Model for Lenvatinib Treatment in Hepatocellular Carcinoma |
title | Development of a Multivariate Prognostic Model for Lenvatinib Treatment in Hepatocellular Carcinoma |
title_full | Development of a Multivariate Prognostic Model for Lenvatinib Treatment in Hepatocellular Carcinoma |
title_fullStr | Development of a Multivariate Prognostic Model for Lenvatinib Treatment in Hepatocellular Carcinoma |
title_full_unstemmed | Development of a Multivariate Prognostic Model for Lenvatinib Treatment in Hepatocellular Carcinoma |
title_short | Development of a Multivariate Prognostic Model for Lenvatinib Treatment in Hepatocellular Carcinoma |
title_sort | development of a multivariate prognostic model for lenvatinib treatment in hepatocellular carcinoma |
topic | Hepatobiliary |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10546830/ https://www.ncbi.nlm.nih.gov/pubmed/37105140 http://dx.doi.org/10.1093/oncolo/oyad107 |
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