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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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Detalles Bibliográficos
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
Descripción
Sumario: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.