Tumor radiomic features on pretreatment MRI to predict response to lenvatinib plus an anti–PD-1 antibody in advanced hepatocellular carcinoma: a multicenter study

Autor: Bin Xu, Sanyuan Dong, Xue-Li Bai, Tian-Qiang Song, Bo-Heng Zhang, Le-Du Zhou, Yong-Jun Chen, Zhi-Ming Zeng, Kui Wang, Hai-Tao Zhao, Na Lu, Wei Zhang, Xu-Bin Li, Su-Su Zheng, Guo Long, Yu-Chen Yang, Hua-Sheng Huang, Lan-Qing Huang, Yun-Chao Wang, Fei Liang, Xiao-Dong Zhu, Cheng Huang, Ying-Hao Shen, Jian Zhou, Meng-Su Zeng, Jia Fan, Sheng-Xiang Rao, Hui-Chuan Sun
Jazyk: angličtina
Rok vydání: 2022
Předmět:
Zdroj: Liver Cancer (2022)
Druh dokumentu: article
ISSN: 2235-1795
1664-5553
DOI: 10.1159/000528034
Popis: Introduction: Lenvatinib plus an anti–PD-1 antibody has shown promising anti-tumor 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, and provide incremental predictive value over clinicopathologic features, and are associated with overall survival and progression-free survival after initiation of this combination regimen.
Databáze: Directory of Open Access Journals
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