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

TITLE=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

JOURNAL=Frontiers in Immunology

VOLUME=Volume 11 - 2020

YEAR=2021

URL=https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2020.613946

DOI=10.3389/fimmu.2020.613946

ISSN=1664-3224

ABSTRACT=Background: There is no study accessible now assessing the prognostic aspect of radiomics for anti-PD-1 therapy for patients with HCC. 
Aim: The 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.
Methods: A 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 Jan 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.
Results: Eight 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.
Conclusions: This 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.