文献类型：期刊文献

英文题名：Application of radiography of computed tomography in non-small cell lung cancer using prognosis model

作者：Jin, Yifeng[1];Lu, Tao[2]

机构：[1]Shaoxing Univ, Dept Resp, Zhuji Affiliated Hosp, Zhuji 311800, Peoples R China;[2]Fujian Med Univ Canc Hosp, Fujian Canc Hosp, Dept Radiol, Fuzhou 350014, Fujian, Peoples R China

年份：2020

卷号：27

期号：4

起止页码：1066

外文期刊名：SAUDI JOURNAL OF BIOLOGICAL SCIENCES

收录：SCI-EXPANDED(收录号：WOS:000524201100008)、、Scopus(收录号：2-s2.0-85081903201)、WOS

语种：英文

外文关键词：Prognostic model; CT radiography; NSCLC; Optimal feature; 5-Fold cross-validation

外文摘要：Objective: Studying the diagnostic value of CT imaging in non-small cell lung cancer (NSCLC), and establishing a prognosis model combined with clinical characteristics is the objective, so as to provide a reference for the survival prediction of NSCLC patients. Method: CT scan data of NSCLC 200 patients were taken as the research object. Through image segmentation, the radiology features of CT images were extracted. The reliability and performance of the prognosis model based on the optimal feature number of specific algorithm and the prognosis model based on the global optimal feature number were compared. Results: 30-RELF-NB (30 optimal features, RELF feature selection algorithm and NB classifier) has the highest accuracy and AUC (area under the subject characteristic curve) in the prognosis model based on the optimal features of specific algorithm. Among the prognosis models based on global optimal features, 25-NB (25 global optimal features, naive Bayes classification algorithm classifier) has the highest accuracy and AUC. Compared with the prediction model based on feature training of specific feature selection algorithm, the overall performance and stability of the prediction model based on global optimal feature are higher. Conclusion: The prognosis model based on the global optimal feature established in this paper has good reliability and performance, and can be applied to the CT radiology of NSCLC. (C) 2020 The Authors. Published by Elsevier B.V. on behalf of King Saud University.