Predicting T Cell-Inflamed Gene Expression Profile in Hepatocellular Carcinoma Based on Dynamic Contrast-Enhanced Ultrasound Radiomics

Abstract

Yijie Wang,1,* Weixiang Weng,2,* Ruiming Liang,3,* Qian Zhou,3 Hangtong Hu,4 Mingde Li,4 Lida Chen,4 Shuling Chen,4 Sui Peng,1,3 Ming Kuang,2,4,5 Han Xiao,6 Wei Wang4 1Department of Gastroenterology and Hepatology, the First Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong Province, People’s Republic of China; 2Center of Hepato-Pancreato-Biliary Surgery, the First Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong Province, People’s Republic of China; 3Clinical Trials Unit, the First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, Guangdong Province, People’s Republic of China; 4Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, the First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, Guangdong Province, People’s Republic of China; 5Cancer Center, the First Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong Province, People’s Republic of China; 6Department of Medical Ultrasonics, the First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, Guangdong Province, People’s Republic of China*These authors contributed equally to this workCorrespondence: Han Xiao, Department of Medical Ultrasonics, the First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, Guangdong Province, People’s Republic of China, Tel/Fax [phone]-8576, Email [email protected] Wei Wang, Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, the First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, Guangdong Province, People’s Republic of China, Tel/Fax [phone]-8576, Email [email protected]: The T cell-inflamed gene expression profile (GEP) quantifies 18 genes’ expression indicative of a T-cell immune tumor microenvironment, playing a crucial role in the immunotherapy of hepatocellular carcinoma (HCC). Our study aims to develop a radiomics-based machine learning model using contrast-enhanced ultrasound (CEUS) for predicting T cell-inflamed GEP in HCC.Methods: The primary cohort of HCC patients with preoperative CEUS and RNA sequencing data of tumor tissues at the single center was used to construct the model. A total of 5936 radiomics features were extracted from the regions of interest in representative images of each phase, and the least absolute shrinkage and selection operator and logistic regression were used to construct four models including three phase-specific models and an integrated model. The area under the curve (AUC) was calculated to evaluate the performance of the model. The independent cohort of HCC patients with preoperative CEUS and Immunoscore based on immunohistochemistry and digital pathology was used to validate the correlation between model prediction value and T-cell infiltration.Results: There were 268 patients enrolled in the primary cohort and 46 patients enrolled in the independent cohort. Compared with the other three models, the AP model constructed by 36 arterial phase (AP) features showed good performance with a mean AUC of 0.905 in the 5-fold cross-validation and was easier to apply in the clinical setting. The decision curve and calibration curve confirmed the clinical utility of the model. In the independent cohort, patients with high Immunoscores showed significantly higher GEP prediction values than those with low Immunoscores (t=− 2.359, p=0.029).Conclusion: The CEUS-based model is a reliable predictive tool for T cell-inflamed GEP in HCC, and might facilitate individualized immunotherapy decision-making.Keywords: Radiomics, Contrast-enhanced ultrasound, Hepatocellular carcinoma, T cell-inflamed gene expression profile

Keywords

• radiomics
• contrast-enhanced ultrasound
• hepatocellular carcinoma
• t cell-inflamed gene expression profile