Evaluation of the value of multiparameter combined analysis of serum markers in the early diagnosis of gastric cancer

作     者：Zhi-Guo Zhang Liang Xu Peng-Jun Zhang Lei Han 

作者机构：Department of OncologyBeijing Daxing District People’s HospitalBeijing 102600China 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4期

页      面：483-491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 the National Key R&D Program of China,No.2016YFC0106604 National Natural Science Foundation of China,No.81502591 

主　　题：Gastric cancer Gastric polyp Serum Artificial neural network Detection 

摘      要：BACKGROUND In early gastric cancer(GC),tumor markers are increased in the *** levels of these markers have been used as important indexes for GC screening,early diagnosis and prognostic ***,specific tumor markers have not yet been *** based on a single tumor marker has limited *** detection rate of GC is still very *** To improve the diagnostic value of blood markers for *** We used a multiparameter joint analysis of 77 indexes of malignant GC and gastric polyp(GP),64 indexes of GC and healthy controls(Ctrls).RESULTS By analyzing the data,there are 27 indexes in the final Ctrls vs GC with P values0.01,the area under the curve(AUC)of albumin is the largest in Ctrls vs GC,and the AUC was 0.907.30 indexes in GP vs GC have P values*** them,the D-dimer showed an AUC of *** 27 indexes in Ctrls vs GC and 30 indexes in GP vs GC were used for binary logistic regression,discriminant analysis,classification tree analysis and artificial neural network analysis *** the ability to distinguish between Ctrls vs GC,GP vs GC,artificial neural networks had better diagnostic value when compared with classification tree,binary logistic regression,and discriminant *** compared Ctrl and GC,the overall prediction accuracy was 92.9%,and the AUC was 0.992(0.980,1.000).When compared GP and GC,the overall prediction accuracy was 77.9%,and the AUC was 0.969(0.948,0.990).CONCLUSION The diagnostic effect of multi-parameter joint artificial neural networks analysis is significantly better than the single-index test diagnosis,and it may provide an assistant method for the detection of GC.