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2023 | OriginalPaper | Chapter

Deep Learning and XGBoost Based Prediction Algorithm for Esophageal Varices

Authors : Xinyi Chen, Jiande Sun, Zhishun Wang, Yanling Fan, Jianping Qiao

Published in: Signal and Information Processing, Networking and Computers

Publisher: Springer Nature Singapore

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Abstract

The deep convolutional neural network has been extensively applied for clinical computer-aided diagnosis. In this study, we combine deep learning feature extraction and eXtreme gradient boosting (XGBoost) classifier for predicting the risk of esophageal varices (EV). First, the quantitative deep learning features and radiomics features of the regions of interest which includes spleen, liver and esophageal are extracted and concatenated. Then, XGBoost and the Least Absolute Shrinkage and Selection Operator (LASSO) are applied for the optimal predictive features selection and prediction of EV risk. XGBoost is used to assess the significance of the extracted features and LASSO is used to select the distinctive features. Finally, random forest, XGBoost and support vector machine classification methods are applied for predicting the low-risk and high-risk of esophageal varices. We collected computed tomography images of cirrhotic patients in two hospitals as the independent training and validation sets. Experimental results show that the features of esophageal are more distinctive than that of other organs. Moreover, the combination of deep learning and radiomics features based on XGBoost algorithm has outperforming classification performance in predicting the severity of EV disease compared to existing approaches.

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Literature
1.
go back to reference Lay, C.S.: Endoscopic variceal ligation in prophylaxis of first variceal bleeding in cirrhotic patients with high-risk esophageal varices. Gastrointest. Endosc. 47(2), 1346–1350 (1998) Lay, C.S.: Endoscopic variceal ligation in prophylaxis of first variceal bleeding in cirrhotic patients with high-risk esophageal varices. Gastrointest. Endosc. 47(2), 1346–1350 (1998)
2.
go back to reference Bourgier, C.: Definition and clinical development. Cancer/Radiothérapie 19(6), 532–537 (2015)CrossRef Bourgier, C.: Definition and clinical development. Cancer/Radiothérapie 19(6), 532–537 (2015)CrossRef
3.
go back to reference Han, X.: Noninvasive evaluation of esophageal varices in cirrhotic patients based on spleen hemodynamics: a dual-energy CT study. Eur. Radiol. 30(6), 3210–3216 (2020)CrossRef Han, X.: Noninvasive evaluation of esophageal varices in cirrhotic patients based on spleen hemodynamics: a dual-energy CT study. Eur. Radiol. 30(6), 3210–3216 (2020)CrossRef
4.
go back to reference Jhang, Z.: Diagnostic value of spleen stiffness by magnetic resonance elastography for prediction of esophageal varices in cirrhotic patients. Abdom. Radiol. 46(2), 526–533 (2020)CrossRef Jhang, Z.: Diagnostic value of spleen stiffness by magnetic resonance elastography for prediction of esophageal varices in cirrhotic patients. Abdom. Radiol. 46(2), 526–533 (2020)CrossRef
5.
go back to reference Liu, F.: Development and validation of a radiomics signature for clinically significant portal hypertension in cirrhosis (CHESS1701): a prospective multicenter study. EBioMedicine 36, 151–158 (2018)CrossRef Liu, F.: Development and validation of a radiomics signature for clinically significant portal hypertension in cirrhosis (CHESS1701): a prospective multicenter study. EBioMedicine 36, 151–158 (2018)CrossRef
6.
go back to reference Li, L.: A multi-organ fusion and LightGBM based radiomics algorithm for high-risk esophageal varices prediction in cirrhotic patients. IEEE Access 9, 15041–15052 (2021)CrossRef Li, L.: A multi-organ fusion and LightGBM based radiomics algorithm for high-risk esophageal varices prediction in cirrhotic patients. IEEE Access 9, 15041–15052 (2021)CrossRef
7.
go back to reference Liu, Y.: Deep convolutional neural network-aided detection of portal hypertension in patients with cirrhosis. Clin. Gastroenterol. Hepatol. 18(13), 2998–3007 (2020)CrossRef Liu, Y.: Deep convolutional neural network-aided detection of portal hypertension in patients with cirrhosis. Clin. Gastroenterol. Hepatol. 18(13), 2998–3007 (2020)CrossRef
8.
go back to reference Chen, M.: Automated and real-time validation of gastroesophageal varices under esophagogastroduodenoscopy using a deep convolutional neural network: a multicenter retrospective study (with video). Gastrointest. Endosc. 93(2), 422–432 (2021)CrossRef Chen, M.: Automated and real-time validation of gastroesophageal varices under esophagogastroduodenoscopy using a deep convolutional neural network: a multicenter retrospective study (with video). Gastrointest. Endosc. 93(2), 422–432 (2021)CrossRef
9.
go back to reference Hong, W.: Use of artificial neural network to predict esophageal varices in patients with HBV related cirrhosis. Hepatitis Monthly 11(7), 544–547 (2011) Hong, W.: Use of artificial neural network to predict esophageal varices in patients with HBV related cirrhosis. Hepatitis Monthly 11(7), 544–547 (2011)
10.
go back to reference Chen, T.: XGBoost: a scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Fransisco, pp. 785–794. ACM (2016) Chen, T.: XGBoost: a scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Fransisco, pp. 785–794. ACM (2016)
Metadata
Title
Deep Learning and XGBoost Based Prediction Algorithm for Esophageal Varices
Authors
Xinyi Chen
Jiande Sun
Zhishun Wang
Yanling Fan
Jianping Qiao
Copyright Year
2023
Publisher
Springer Nature Singapore
DOI
https://doi.org/10.1007/978-981-19-3387-5_134