Frontiers in Immunology (Jan 2021)

Development and Validation of a Contrast-Enhanced CT-Based Radiomics Nomogram for Prediction of Therapeutic Efficacy of Anti-PD-1 Antibodies in Advanced HCC Patients

  • Guosheng Yuan,
  • Yangda Song,
  • Qi Li,
  • Qi Li,
  • Xiaoyun Hu,
  • Mengya Zang,
  • Wencong Dai,
  • Xiao Cheng,
  • Wei Huang,
  • Wenxuan Yu,
  • Mian Chen,
  • Yabing Guo,
  • Qifan Zhang,
  • Jinzhang Chen,
  • Jinzhang Chen

DOI
https://doi.org/10.3389/fimmu.2020.613946
Journal volume & issue
Vol. 11

Abstract

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BackgroundThere is no study accessible now assessing the prognostic aspect of radiomics for anti-PD-1 therapy for patients with HCC.AimThe aim of this study was to develop and validate a radiomics nomogram by incorporating the pretreatment contrast-enhanced Computed tomography (CT) images and clinical risk factors to estimate the anti-PD-1 treatment efficacy in Hepatocellular Carcinoma (HCC) patients.MethodsA total of 58 patients with advanced HCC who were refractory to the standard first-line of therapy, and received PD-1 inhibitor treatment with Toripalimab, Camrelizumab, or Sintilimab from 1st January 2019 to 31 July 2020 were enrolled and divided into two sets randomly: training set (n = 40) and validation set (n = 18). Radiomics features were extracted from non-enhanced and contrast-enhanced CT scans and selected by using the least absolute shrinkage and selection operator (LASSO) method. Finally, a radiomics nomogram was developed based on by univariate and multivariate logistic regression analysis. The performance of the nomogram was evaluated by discrimination, calibration, and clinical utility.ResultsEight radiomics features from the whole tumor and peritumoral regions were selected and comprised of the Fusion Radiomics score. Together with two clinical factors (tumor embolus and ALBI grade), a radiomics nomogram was developed with an area under the curve (AUC) of 0.894 (95% CI, 0.797–0.991) and 0.883 (95% CI, 0.716–0.998) in the training and validation cohort, respectively. The calibration curve and decision curve analysis (DCA) confirmed that nomogram had good consistency and clinical usefulness.ConclusionsThis study has developed and validated a radiomics nomogram by incorporating the pretreatment CECT images and clinical factors to predict the anti-PD-1 treatment efficacy in patients with advanced HCC.

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