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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

Authors :
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
Source :
Liver Cancer; December 2022, Vol. 12 Issue: 3 p262-276, 15p
Publication Year :
2022

Abstract

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.

Details

Language :
English
ISSN :
22351795 and 16645553
Volume :
12
Issue :
3
Database :
Supplemental Index
Journal :
Liver Cancer
Publication Type :
Periodical
Accession number :
ejs63719806
Full Text :
https://doi.org/10.1159/000528034