ArticleWorld journal of gastroenterology2022
Radiomics signature: A potential biomarker for β-arrestin1 phosphorylation prediction in hepatocellular carcinoma.
Article in World journal of gastroenterology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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8 citing papers in PubMed, 11 citations in OpenAlex.
- Deep Reinforcement Learning for CT-Based Non-Invasive Prediction of SOX9 Expression in Hepatocellular Carcinoma.Diagnostics (Basel, Switzerland) · 2025Article
- [Research progress of radiomics in hepatocellular carcinoma].Zhonghua gan zang bing za zhi = Zhonghua ganzangbing zazhi = Chinese journal of hepatology · 2024Review
- CT-based radiomics predicts CD38 expression and indirectly reflects clinical prognosis in epithelial ovarian cancer.Heliyon · 2024Article
- Machine learning based ultrasomics noninvasive predicting EGFR expression status in hepatocellular carcinoma patients.Frontiers in medicine · 2024Article
- Computed tomography radiomics signature via machine learning predictsJournal of gastrointestinal oncology · 2023Article
- Preoperative prediction of macrotrabecular-massive hepatocellular carcinoma through dynamic contrast-enhanced magnetic resonance imaging-based radiomics.World journal of gastroenterology · 2023Article
- Current status and future perspectives of radiomics in hepatocellular carcinoma.World journal of gastroenterology · 2023Review
- Prognostic Role of Molecular and Imaging Biomarkers for Predicting Advanced Hepatocellular Carcinoma Treatment Efficacy.Cancers · 2022Review
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Authors and funding
9 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe 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.
aimTo develop and validate radiomics-based models for predicting β-arrestin1 phosphorylation in HCC using radiomics with contrast-enhanced CT.
methodsNinety-nine HCC patients (training cohort:
resultsFour 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;
conclusionThe 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.
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