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Tumor Radiomic Features on Pretreatment MRI to Predict Response to Lenvatinib plus an Anti-PD-1 Antibody in Advanced Hepatocellular Carcinoma: A Multicenter Study

INTRODUCTION: Lenvatinib plus an anti-PD-1 antibody has shown promising antitumor effects in patients with advanced hepatocellular carcinoma (HCC), but with clinical benefit limited to a subset of patients. We developed and validated a radiomic-based model to predict objective response to this combi...

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Autores principales: Xu, Bin, Dong, San-Yuan, Bai, Xue-Li, Song, Tian-Qiang, Zhang, Bo-Heng, Zhou, Le-Du, Chen, Yong-Jun, Zeng, Zhi-Ming, Wang, Kui, Zhao, Hai-Tao, Lu, Na, Zhang, Wei, Li, Xu-Bin, Zheng, Su-Su, Long, Guo, Yang, Yu-Chen, Huang, Hua-Sheng, Huang, Lan-Qing, Wang, Yun-Chao, Liang, Fei, Zhu, Xiao-Dong, Huang, Cheng, Shen, Ying-Hao, Zhou, Jian, Zeng, Meng-Su, Fan, Jia, Rao, Sheng-Xiang, Sun, Hui-Chuan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: S. Karger AG 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10433098/
https://www.ncbi.nlm.nih.gov/pubmed/37601982
http://dx.doi.org/10.1159/000528034
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author Xu, Bin
Dong, San-Yuan
Bai, Xue-Li
Song, Tian-Qiang
Zhang, Bo-Heng
Zhou, Le-Du
Chen, Yong-Jun
Zeng, Zhi-Ming
Wang, Kui
Zhao, Hai-Tao
Lu, Na
Zhang, Wei
Li, Xu-Bin
Zheng, Su-Su
Long, Guo
Yang, Yu-Chen
Huang, Hua-Sheng
Huang, Lan-Qing
Wang, Yun-Chao
Liang, Fei
Zhu, Xiao-Dong
Huang, Cheng
Shen, Ying-Hao
Zhou, Jian
Zeng, Meng-Su
Fan, Jia
Rao, Sheng-Xiang
Sun, Hui-Chuan
author_facet Xu, Bin
Dong, San-Yuan
Bai, Xue-Li
Song, Tian-Qiang
Zhang, Bo-Heng
Zhou, Le-Du
Chen, Yong-Jun
Zeng, Zhi-Ming
Wang, Kui
Zhao, Hai-Tao
Lu, Na
Zhang, Wei
Li, Xu-Bin
Zheng, Su-Su
Long, Guo
Yang, Yu-Chen
Huang, Hua-Sheng
Huang, Lan-Qing
Wang, Yun-Chao
Liang, Fei
Zhu, Xiao-Dong
Huang, Cheng
Shen, Ying-Hao
Zhou, Jian
Zeng, Meng-Su
Fan, Jia
Rao, Sheng-Xiang
Sun, Hui-Chuan
author_sort Xu, Bin
collection PubMed
description INTRODUCTION: Lenvatinib plus an anti-PD-1 antibody has shown promising antitumor effects in patients with advanced hepatocellular carcinoma (HCC), but with clinical benefit limited to a subset of patients. We developed and validated a radiomic-based model to predict objective response to this combination therapy in advanced HCC patients. METHODS: Patients (N = 170) who received first-line combination therapy with lenvatinib plus an anti-PD-1 antibody were retrospectively enrolled from 9 Chinese centers; 124 and 46 into the training and validation cohorts, respectively. Radiomic features were extracted from pretreatment contrast-enhanced MRI. After feature selection, clinicopathologic, radiomic, and clinicopathologic-radiomic models were built using a neural network. The performance of models, incremental predictive value of radiomic features compared with clinicopathologic features and relationship between radiomic features and survivals were assessed. RESULTS: The clinicopathologic model modestly predicted objective response with an AUC of 0.748 (95% CI: 0.656–0.840) and 0.702 (95% CI: 0.547–0.884) in the training and validation cohorts, respectively. The radiomic model predicted response with an AUC of 0.886 (95% CI: 0.815–0.957) and 0.820 (95% CI: 0.648–0.984), respectively, with good calibration and clinical utility. The incremental predictive value of radiomic features to clinicopathologic features was confirmed with a net reclassification index of 47.9% (p < 0.001) and 41.5% (p = 0.025) in the training and validation cohorts, respectively. Furthermore, radiomic features were associated with overall survival and progression-free survival both in the training and validation cohorts, but modified albumin-bilirubin grade and neutrophil-to-lymphocyte ratio were not. CONCLUSION: Radiomic features extracted from pretreatment MRI can predict individualized objective response to combination therapy with lenvatinib plus an anti-PD-1 antibody in patients with unresectable or advanced HCC, provide incremental predictive value over clinicopathologic features, and are associated with overall survival and progression-free survival after initiation of this combination regimen.
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spelling pubmed-104330982023-08-18 Tumor Radiomic Features on Pretreatment MRI to Predict Response to Lenvatinib plus an Anti-PD-1 Antibody in Advanced Hepatocellular Carcinoma: A Multicenter Study Xu, Bin Dong, San-Yuan Bai, Xue-Li Song, Tian-Qiang Zhang, Bo-Heng Zhou, Le-Du Chen, Yong-Jun Zeng, Zhi-Ming Wang, Kui Zhao, Hai-Tao Lu, Na Zhang, Wei Li, Xu-Bin Zheng, Su-Su Long, Guo Yang, Yu-Chen Huang, Hua-Sheng Huang, Lan-Qing Wang, Yun-Chao Liang, Fei Zhu, Xiao-Dong Huang, Cheng Shen, Ying-Hao Zhou, Jian Zeng, Meng-Su Fan, Jia Rao, Sheng-Xiang Sun, Hui-Chuan Liver Cancer Research Article INTRODUCTION: Lenvatinib plus an anti-PD-1 antibody has shown promising antitumor effects in patients with advanced hepatocellular carcinoma (HCC), but with clinical benefit limited to a subset of patients. We developed and validated a radiomic-based model to predict objective response to this combination therapy in advanced HCC patients. METHODS: Patients (N = 170) who received first-line combination therapy with lenvatinib plus an anti-PD-1 antibody were retrospectively enrolled from 9 Chinese centers; 124 and 46 into the training and validation cohorts, respectively. Radiomic features were extracted from pretreatment contrast-enhanced MRI. After feature selection, clinicopathologic, radiomic, and clinicopathologic-radiomic models were built using a neural network. The performance of models, incremental predictive value of radiomic features compared with clinicopathologic features and relationship between radiomic features and survivals were assessed. RESULTS: The clinicopathologic model modestly predicted objective response with an AUC of 0.748 (95% CI: 0.656–0.840) and 0.702 (95% CI: 0.547–0.884) in the training and validation cohorts, respectively. The radiomic model predicted response with an AUC of 0.886 (95% CI: 0.815–0.957) and 0.820 (95% CI: 0.648–0.984), respectively, with good calibration and clinical utility. The incremental predictive value of radiomic features to clinicopathologic features was confirmed with a net reclassification index of 47.9% (p < 0.001) and 41.5% (p = 0.025) in the training and validation cohorts, respectively. Furthermore, radiomic features were associated with overall survival and progression-free survival both in the training and validation cohorts, but modified albumin-bilirubin grade and neutrophil-to-lymphocyte ratio were not. CONCLUSION: Radiomic features extracted from pretreatment MRI can predict individualized objective response to combination therapy with lenvatinib plus an anti-PD-1 antibody in patients with unresectable or advanced HCC, provide incremental predictive value over clinicopathologic features, and are associated with overall survival and progression-free survival after initiation of this combination regimen. S. Karger AG 2022-11-28 /pmc/articles/PMC10433098/ /pubmed/37601982 http://dx.doi.org/10.1159/000528034 Text en Copyright © 2022 by The Author(s). Published by S. Karger AG, Basel https://creativecommons.org/licenses/by-nc/4.0/This article is licensed under the Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC). Usage and distribution for commercial purposes requires written permission.
spellingShingle Research Article
Xu, Bin
Dong, San-Yuan
Bai, Xue-Li
Song, Tian-Qiang
Zhang, Bo-Heng
Zhou, Le-Du
Chen, Yong-Jun
Zeng, Zhi-Ming
Wang, Kui
Zhao, Hai-Tao
Lu, Na
Zhang, Wei
Li, Xu-Bin
Zheng, Su-Su
Long, Guo
Yang, Yu-Chen
Huang, Hua-Sheng
Huang, Lan-Qing
Wang, Yun-Chao
Liang, Fei
Zhu, Xiao-Dong
Huang, Cheng
Shen, Ying-Hao
Zhou, Jian
Zeng, Meng-Su
Fan, Jia
Rao, Sheng-Xiang
Sun, Hui-Chuan
Tumor Radiomic Features on Pretreatment MRI to Predict Response to Lenvatinib plus an Anti-PD-1 Antibody in Advanced Hepatocellular Carcinoma: A Multicenter Study
title Tumor Radiomic Features on Pretreatment MRI to Predict Response to Lenvatinib plus an Anti-PD-1 Antibody in Advanced Hepatocellular Carcinoma: A Multicenter Study
title_full Tumor Radiomic Features on Pretreatment MRI to Predict Response to Lenvatinib plus an Anti-PD-1 Antibody in Advanced Hepatocellular Carcinoma: A Multicenter Study
title_fullStr Tumor Radiomic Features on Pretreatment MRI to Predict Response to Lenvatinib plus an Anti-PD-1 Antibody in Advanced Hepatocellular Carcinoma: A Multicenter Study
title_full_unstemmed Tumor Radiomic Features on Pretreatment MRI to Predict Response to Lenvatinib plus an Anti-PD-1 Antibody in Advanced Hepatocellular Carcinoma: A Multicenter Study
title_short Tumor Radiomic Features on Pretreatment MRI to Predict Response to Lenvatinib plus an Anti-PD-1 Antibody in Advanced Hepatocellular Carcinoma: A Multicenter Study
title_sort tumor radiomic features on pretreatment mri to predict response to lenvatinib plus an anti-pd-1 antibody in advanced hepatocellular carcinoma: a multicenter study
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10433098/
https://www.ncbi.nlm.nih.gov/pubmed/37601982
http://dx.doi.org/10.1159/000528034
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