Yifei Huang1, Fangze Huang1, Li Yang2, Weiling Hu3, Yanna Liu4, Zihuai Lin1, Xiangpan Meng5, Manling Zeng1, Chaohui He6, Qing Xu2, Guanghang Xie1, Chuan Liu1, Mingkai Liang4, Xiaoguo Li4, Ning Kang4, Dan Xu4, Jitao Wang4, Liting Zhang4, Xiaorong Mao4, Changqing Yang2, Ming Xu7, Xiaolong Qi4, Hua Mao1. 1. Department of Gastroenterology, Zhujiang Hospital, Southern Medical University, Guangzhou, China. 2. Division of Gastroenterology and Hepatology, Digestive Disease Institute, Shanghai Tongji Hospital, Tongji University School of Medicine, Shanghai, China. 3. Department of Gastroenterology, Sir Run Shaw Hospital, Zhejiang University, Hangzhou, China. 4. CHESS Center, Institute of Portal Hypertension, The First Hospital of Lanzhou University, Lanzhou, China. 5. Jiangsu Key Laboratory of Molecular and Functional Imaging, Department of Radiology, Zhongda Hospital, Medical School of Southeast University, Nanjing, China. 6. Department of Gastroenterology, The Fifth Affiliated Hospital of Zunyi Medical University, Zhuhai, China. 7. Department of Gastroenterology, Guangdong Second Provincial General Hospital, Guangzhou, China.
Abstract
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.
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.