Application of radiography of computed tomography in non-small cell lung cancer using prognosis model |
| |
Affiliation: | 1. Department of Respiratory, Zhuji Affiliated Hospital of Shaoxing University, Zhuji 311800, China;2. Department of Radiology, Fujian Cancer Hospital, Fujian Medical University Cancer Hospital, Fuzhou City, Fujiang Province 350014, China |
| |
Abstract: | ObjectiveStudying 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.MethodCT 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.Results30-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.ConclusionThe 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. |
| |
Keywords: | Prognostic model CT radiography NSCLC Optimal feature 5-Fold cross-validation |
本文献已被 ScienceDirect 等数据库收录! |
|