Radiomics signature: A potential biomarker for β-arrestin1 phosphorylation prediction in hepatocellular carcinoma

作者：Feng Che, Qing Xu, Qian Li, Zi-Xing Huang, Caiwei Yang, Li Ye Wang, Yi Wei, Yujun Shi, Bin Song · 发表于：World Journal of Gastroenterology · 年份：2022 · DOI：10.3748/wjg.v28.i14.1479 · 被引用次数：11 · 研究领域：Radiomics and Machine Learning in Medical Imaging、Hepatocellular Carcinoma Treatment and Prognosis、Peptidase Inhibition and Analysis

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: = 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: < 0.05). CONCLUSION: The radiomics signature is a reliable tool for evaluating β...