Liver Cancer (Nov 2022)

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

  • 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

DOI
https://doi.org/10.1159/000528034

Abstract

Read online

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.