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Deying Xie

As seen in: Wiley Online Library, BMC Cancer
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Articles

Machine learning-based differentiation of benign and malignant adrenal lesions using 18F-FDG PET/CT: a two-stage classification and SHAP interpretation study | BMC Cancer | Full Text

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By Yun Wang, Yuqi Su, Jing Li, Deying Xie, Zhuolin Liu, Chengyang Sun, Jingjing Zhang, Heqing Yi, Ye Yuan, Yuhuang Cai, Jaesik Jeong
| BMC Cancer Verified
This study aimed to explore the feasibility and clinical utility of applying interpretable machine learning algorithms to classify adrenal lesions using quantitative features derived from 18 F-FDG PET/CT imaging and clinical variables. The research was structured into two major predictive tasks: first, the binary classification of adrenal lesions as benign or malignant; and second, the subclassification of malignant lesions into lung cancer metastases and lymphoma.
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Pediatric anesthesia in China

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By Liu Qi Zhang, Sichuan Chengdu, Deying Xie, Dongxu Lei
| Wiley Online Library Verified
Supporting Information Filename Description pan14902-sup-0001-AppendixS1.docxWord 2007 document , 15 KB Appendix S1. REFERENCES 1, , , et al. Measuring performance on the Healthcare Access and Quality Index for 195 countries and territories and selected subnational locations: a systematic analysis from the global burden of disease study 2016. Lancet. 2018; 391(10136): 2236-2271. 2, , , et al. Anaesthesiology in China: a cross-sectional survey of the current status of anaesthesiology departments.
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