Development and validation of a radiomics signature as a non‐invasive complementary predictor of gastroesophageal varices and high‐risk varices in compensated advanced chronic liver disease: A multicenter study
| Autor: | Hua Mao, Xiaolong Qi, Manling Zeng, Li Yang, Yifei Huang, Jitao Wang, Fangze Huang, Chaohui He, Ning Kang, Yanna Liu, Weiling Hu, Chuan Liu, Ming Xu, Guanghang Xie, Xiaoguo Li, Zihuai Lin, Mingkai Liang, Xiaorong Mao, Liting Zhang, Qing Xu, Xiang-Pan Meng, Dan Xu, Changqing Yang |
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| Rok vydání: | 2020 |
| Předmět: |
Adult
Male Risk medicine.medical_specialty Esophageal and Gastric Varices Chronic liver disease Severity of Illness Index 03 medical and health sciences 0302 clinical medicine Multicenter trial medicine Humans Prospective Studies Hepatology Receiver operating characteristic medicine.diagnostic_test Esophagogastroduodenoscopy business.industry Liver Diseases Gastroenterology Gold standard (test) Middle Aged medicine.disease Confidence interval Endoscopy ROC Curve 030220 oncology & carcinogenesis Chronic Disease Female 030211 gastroenterology & hepatology Radiology Tomography X-Ray Computed Varices business |
| Zdroj: | Journal of Gastroenterology and Hepatology. 36:1562-1570 |
| ISSN: | 1440-1746 0815-9319 |
| DOI: | 10.1111/jgh.15306 |
| Popis: | BACKGROUND AND AIM Gastroesophageal varices (GEV) present in compensated advanced chronic liver disease (cACLD) and can develop into high-risk varices (HRV). The gold standard for diagnosing GEV is esophagogastroduodenoscopy (EGD). However, EGD is invasive and less tolerant. This study aimed to develop and validate radiomics signatures based on noncontrast-enhanced computed tomography (CT) images for non-invasive diagnosis of GEV and HRV in patients with cACLD. METHODS The multicenter trial enrolled 161 patients with cACLD from six university hospitals in China between January 2015 and September 2019, who underwent both EGD and noncontrast-enhanced CT examination within 14 days prior to the endoscopy. Two radiomics signatures, termed rGEV and rHRV, respectively, were built based on CT images in a training cohort of 129 patients and validated in a prospective validation cohort of 32 patients (ClinicalTrials. gov identifier: NCT03749954). RESULTS In the training cohort, both rGEV and rHRV exhibited high discriminative abilities on determining the existence of GEV and HRV with the area under receiver operating characteristic curve (AUC) of 0.941 (95% confidence interval [CI] 0.904-0.978) and 0.836 (95% CI 0.766-0.905), respectively. In validation cohort, rGEV and rHRV showed high discriminative abilities with AUCs of 0.871 (95% CI 0.739-1.000) and 0.831 (95% CI 0.685-0.978), respectively. CONCLUSIONS This study demonstrated that rGEV and rHRV could serve as the satisfying auxiliary parameters for detection of GEV and HRV with good diagnostic performance. |
| Databáze: | OpenAIRE |
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