Clinical value evaluation of serum markers for early diagnosis of colorectal cancer

作     者：Wen-Yue Song Xin Zhang Qi Zhang Peng-Jun Zhang Rong Zhang 

作者机构：School of Life Science and BiopharmaceuticsShenyang Pharmaceutical UniversityShenyang 110016Liaoning ProvinceChina Key Laboratory of Carcinogenesis and Translational Research(Ministry of Education/Beijing)Interventional Therapy DepartmentPeking University Cancer Hospital and InstituteBeijing 100142China 

出 版 物：《World Journal of Gastrointestinal Oncology》 (世界胃肠肿瘤学杂志（英文版）（电子版）)

年 卷 期：2020年第12卷第2期

页      面：219-227页

核心收录：

学科分类：1002[医学-临床医学] 100214[医学-肿瘤学] 10[医学] 

基　　金：Supported by National Key R and D Program of China,No.2016YFC0106604 National Natural Science Foundation of China,No.81502591 

主　　题：Colorectal cancer Serum Index Biomarker Artificial neural network 

摘      要：BACKGROUND Early screening for colorectal cancer(CRC)is important in clinical ***,the currently methods are inadequate because of high cost and low diagnostic *** To develop a new examination method based on the serum biomarker panel for the early detection of *** Three hundred and fifty cases of CRC,300 cases of colorectal polyps and 360 cases of normal *** with the results of area under curve(AUC)and correlation analysis,the binary Logistic regression analysis of the remaining indexes which is in accordance with the requirements was carried out,and discriminant analysis,classification tree and artificial neural network analysis were used to analyze the remaining indexes at the same *** By comparison of these methods,we obtained the ability to distinguish CRC from healthy control group,malignant disease group and benign disease *** neural network had the best diagnostic value when compared with binary logistic regression,discriminant analysis,and classification *** AUC of CRC and the control group was 0.992(0.987,0.997),sensitivity and specificity were 98.9%and 95.6%.The AUC of the malignant disease group and benign group was 0.996(0.992,0.999),sensitivity and specificity were 97.4%and 96.7%.CONCLUSION Artificial neural network diagnosis method can improve the sensitivity and specificity of the diagnosis of CRC,and a novel assistant diagnostic method was built for the early detection of CRC.