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

Abstract

Purpose

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

Abbreviations

AFP, alpha fetoprotein; AP, arterial phase; AUC, area under the curve; BOR, best overall response; CEA, carcinoembryonic antigen; CEUS, contrast-enhanced ultrasound; DICOM, digital imaging and communication in medicine; DP, delayed phase; GEP, gene expression profile; HCC, hepatocellular carcinoma; ICC, intragroup correlation coefficient; ICIs, immune checkpoint inhibitors; LASSO, the least absolute shrinkage and selection operator; MSI, microsatellite instability; PD-1, programmed death receptor 1; PD-L1, programmed death-ligand 1; PVP, portal venous phase; RNA-seq, ribonucleic acid sequencing; ROC, receiver operating curve; TLS, tertiary lymphoid structures; TMB, tumor mutation burden; TME, tumor microenvironment.

Ethics Approval and Informed Consent

This article does not contain any studies with animals performed by any of the authors. This study was approved by the ethics committees of the First Affiliated Hospital of Sun Yat-sen University (Approval number: [2021]152) and fully complied with the Declaration of Helsinki and the Guideline for Good Clinical Practice; written informed consent was obtained from each patient before collecting their tumor samples.

Consent for Publication

Informed consent for publication was obtained from all authors.

Acknowledgments

We thank all the patients for their participation in this study.

Author Contributions

All authors made a significant contribution to the work reported, whether that is in the conception, study design, execution, acquisition of data, analysis and interpretation, or in all these areas; took part in drafting, revising or critically reviewing the article; gave final approval of the version to be published; have agreed on the journal to which the article has been submitted; and agree to be accountable for all aspects of the work.

Disclosure

The authors report no conflicts of interest in this work.

Additional information

Funding

This study has received funding by the National Key Research and Development Program of China (No. 2020AAA0109504), National Natural Science Foundation of China (No. 82102141, NO. 82272076) and Guangdong Natural Science Foundation (NO. 2022A1515011148).