Retrospective Study
Copyright ©The Author(s) 2022. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastroenterol. Apr 14, 2022; 28(14): 1479-1493
Published online Apr 14, 2022. doi: 10.3748/wjg.v28.i14.1479
Radiomics signature: A potential biomarker for β-arrestin1 phosphorylation prediction in hepatocellular carcinoma
Feng Che, Qing Xu, Qian Li, Zi-Xing Huang, Cai-Wei Yang, Li Ye Wang, Yi Wei, Yu-Jun Shi, Bin Song
Feng Che, Qian Li, Zi-Xing Huang, Cai-Wei Yang, Yi Wei, Bin Song, Department of Radiology, West China Hospital, Sichuan University, Chengdu 610041, Sichuan Province, China
Qing Xu, Yu-Jun Shi, Institute of Clinical Pathology, West China Hospital, Sichuan University, Chengdu 610041, Sichuan Province, China
Li Ye Wang, Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd, Shanghai 200232, China
Author contributions: Che F, Xu Q, Shi YJ and Song B designed the research; Che F, Li Q and Xu Q conducted literature search and analysis; Yang CW, Huang ZX, Wang LY and Wei Y provided material support; Song B provided funding for the article; Che F and Xu Q wrote the paper; Che F and Xu Q contributed equally to this work.
Supported by the Science and Technology Support Program of Sichuan Province, No. 2021YFS0144 and No. 2021YFS0021; China Postdoctoral Science Foundation, No. 2021M692289; and National Natural Science Foundation of China, No. 81971571.
Institutional review board statement: This study was approved by the Ethics Committee of West China Hospital.
Informed consent statement: Patients were not required to give informed consent to the study because this retrospective study used anonymous clinical data that were obtained after each patient agreed to treatment by written consent.
Conflict-of-interest statement: We have no financial relationships to disclose.
Data sharing statement: No additional data are available.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Bin Song, MD, Chief Doctor, Doctor, Professor, Department of Radiology, West China Hospital, Sichuan University, No 37, Guoxue Alley, Wuhou District, Chengdu 610041, Sichuan Province, China. songlab_radiology@163.com
Received: November 9, 2021
Peer-review started: November 9, 2021
First decision: January 9, 2022
Revised: January 22, 2022
Accepted: March 6, 2022
Article in press: March 6, 2022
Published online: April 14, 2022
Processing time: 148 Days and 4.7 Hours
Abstract
BACKGROUND

The phosphorylation status of β-arrestin1 influences its function as a signal strongly related to sorafenib resistance. This retrospective study aimed to develop and validate radiomics-based models for predicting β-arrestin1 phosphorylation in hepatocellular carcinoma (HCC) using whole-lesion radiomics and visual imaging features on preoperative contrast-enhanced computed tomography (CT) images.

AIM

To develop and validate radiomics-based models for predicting β-arrestin1 phosphorylation in HCC using radiomics with contrast-enhanced CT.

METHODS

Ninety-nine HCC patients (training cohort: n = 69; validation cohort: n = 30) receiving systemic sorafenib treatment after surgery were enrolled in this retrospective study. Three-dimensional whole-lesion regions of interest were manually delineated along the tumor margins on portal venous CT images. Radiomics features were generated and selected to build a radiomics score using logistic regression analysis. Imaging features were evaluated by two radiologists independently. All these features were combined to establish clinico-radiological (CR) and clinico-radiological-radiomics (CRR) models by using multivariable logistic regression analysis. The diagnostic performance and clinical usefulness of the models were measured by receiver operating characteristic and decision curves, and the area under the curve (AUC) was determined. Their association with prognosis was evaluated using the Kaplan-Meier method.

RESULTS

Four radiomics features were selected to construct the radiomics score. In the multivariate analysis, alanine aminotransferase level, tumor size and tumor margin on portal venous phase images were found to be significant independent factors for predicting β-arrestin1 phosphorylation-positive HCC and were included in the CR model. The CRR model integrating the radiomics score with clinico-radiological risk factors showed better discriminative performance (AUC = 0.898, 95%CI, 0.820 to 0.977) than the CR model (AUC = 0.794, 95%CI, 0.686 to 0.901; P = 0.011), with increased clinical usefulness confirmed in both the training and validation cohorts using decision curve analysis. The risk of β-arrestin1 phosphorylation predicted by the CRR model was significantly associated with overall survival in the training and validation cohorts (log-rank test, P < 0.05).

CONCLUSION

The radiomics signature is a reliable tool for evaluating β-arrestin1 phosphorylation which has prognostic significance for HCC patients, providing the potential to better identify patients who would benefit from sorafenib treatment.

Keywords: Hepatocellular carcinoma; Sorafenib resistance; β-Arrestin1 phosphorylation; Radiomics; Computed tomography; Overall survival

Core Tip: The aim of this study was to develop and validate radiomics-based models for predicting β-arrestin1 phosphorylation in hepatocellular carcinoma (HCC). A total of 99 HCC patients (training cohort: n = 69; validation cohort: n = 30) were included, and the final clinico-radiological-radiomics model integrating the radiomics scores and clinico-radiological risk factors showed satisfactory discriminative performance (AUC = 0.898, 95%CI, 0.820 to 0.977). The preoperative prediction model can be used as a noninvasive and effective tool to help predict the outcome of HCC patients treated with sorafenib and identify patients who would benefit most from sorafenib treatment.