Yiken Lin1, Lijuan Li2, Dexin Yu3, Zhuyun Liu3, Shuhong Zhang4, Qiuzhi Wang4, Yueyue Li1, Baoquan Cheng1, Jianping Qiao5, Yanjing Gao6. 1. Department of Gastroenterology, Qilu Hospital, Cheloo College of Medicine, Shandong University, Wenhua Xi Road, 107, Jinan, 250012, Shandong, China. 2. Shandong Province Key Laboratory of Medical Physics and Image Processing Technology, School of Physics and Electronics, Shandong Normal University, Jinan, Shandong, China. 3. Department of Radiology, Qilu Hospital, Cheloo College of Medicine, Shandong University, Jinan, Shandong, China. 4. Department of Hepatology, Jinan Central Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China. 5. Shandong Province Key Laboratory of Medical Physics and Image Processing Technology, School of Physics and Electronics, Shandong Normal University, Jinan, Shandong, China. jpqiao@sdu.edu.cn. 6. Department of Gastroenterology, Qilu Hospital, Cheloo College of Medicine, Shandong University, Wenhua Xi Road, 107, Jinan, 250012, Shandong, China. gaoyanjing@sdu.edu.cn.
Abstract
BACKGROUND AND AIMS: Highly accurate noninvasive methods for predicting gastroesophageal varices needing treatment (VNT) are desired. Radiomics is a newly emerging technology of image analysis. This study aims to develop and validate a novel noninvasive method based on radiomics for predicting VNT in cirrhosis. METHODS: In this retrospective-prospective study, a total of 245 cirrhotic patients were divided as the training set, internal validation set and external validation set. Radiomics features were extracted from portal-phase computed tomography (CT) images of each patient. A radiomics signature (Rad score) was constructed with the least absolute shrinkage and selection operator algorithm and tenfold cross-validation in the training set. Combined with independent risk factors, a radiomics nomogram was built with a multivariate logistic regression model. RESULTS: The Rad score, consisting of 14 features from the gastroesophageal region and 5 from the splenic hilum region, was effective for VNT classification. The diagnostic performance was further improved by combining the Rad score with platelet counts, achieving an AUC of 0.987 (95% CI 0.969-1.00), 0.973 (95% CI 0.939-1.00) and 0.947 (95% CI 0.876-1.00) in the training set, internal validation set and external validation set, respectively. In efficacy and safety assessment, the radiomics nomogram could spare more than 40% of endoscopic examinations with a low risk of missing VNT (< 5%), and no more than 8.3% of unnecessary endoscopic examinations still be performed. CONCLUSIONS: In this study, we developed and validated a novel, diagnostic radiomics-based nomogram which is a reliable and noninvasive method to predict VNT in cirrhotic patients. CLINICAL TRIALS REGISTRATION: NCT04210297.
BACKGROUND AND AIMS: Highly accurate noninvasive methods for predicting gastroesophageal varices needing treatment (VNT) are desired. Radiomics is a newly emerging technology of image analysis. This study aims to develop and validate a novel noninvasive method based on radiomics for predicting VNT in cirrhosis. METHODS: In this retrospective-prospective study, a total of 245 cirrhotic patients were divided as the training set, internal validation set and external validation set. Radiomics features were extracted from portal-phase computed tomography (CT) images of each patient. A radiomics signature (Rad score) was constructed with the least absolute shrinkage and selection operator algorithm and tenfold cross-validation in the training set. Combined with independent risk factors, a radiomics nomogram was built with a multivariate logistic regression model. RESULTS: The Rad score, consisting of 14 features from the gastroesophageal region and 5 from the splenic hilum region, was effective for VNT classification. The diagnostic performance was further improved by combining the Rad score with platelet counts, achieving an AUC of 0.987 (95% CI 0.969-1.00), 0.973 (95% CI 0.939-1.00) and 0.947 (95% CI 0.876-1.00) in the training set, internal validation set and external validation set, respectively. In efficacy and safety assessment, the radiomics nomogram could spare more than 40% of endoscopic examinations with a low risk of missing VNT (< 5%), and no more than 8.3% of unnecessary endoscopic examinations still be performed. CONCLUSIONS: In this study, we developed and validated a novel, diagnostic radiomics-based nomogram which is a reliable and noninvasive method to predict VNT in cirrhotic patients. CLINICAL TRIALS REGISTRATION: NCT04210297.
Authors: Jérémy Dana; Aïna Venkatasamy; Antonio Saviano; Joachim Lupberger; Yujin Hoshida; Valérie Vilgrain; Pierre Nahon; Caroline Reinhold; Benoit Gallix; Thomas F Baumert Journal: Hepatol Int Date: 2022-02-09 Impact factor: 9.029