Diagnosis of pancreatic carcinoma based on combined measurement of multiple serum tumor markers using artificial neural network analysis

作     者：Yang Yingchi Chen Hui Wang Dong Luo Wei Zhu Biyun Zhang Zhongtao 

作者机构：Department of General Surgery Beijing Friendship Hospital Capital Medical University Beijing 100050 China Institute of Biomedical Engineering Capital Medical UniversityBeijing 100069 China 

出 版 物：《Chinese Medical Journal》 (中华医学杂志（英文版）)

年 卷 期：2014年第127卷第10期

页      面：1891-1896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] 

主　　题：artificial neural network tumor markers pancreatic cancer Logistic regression 

摘      要：Background Artificial neural network （ANN） has demonstrated the ability to assimilate information from multiple sources to enable the detection of subtle and complex *** this reseamh,we evaluated an ANN model in the diagnosis of pancreatic cancer using multiple serum *** In this retrospective analysis,913 serum specimens collected at the Department of General Surgery of Beijing Friendship Hospital were analyzed for carbohydrate antigen 19-9 （CA19-9）,carbohydrate antigen 125 （CA125）,and caminoembryonic antigen （CEA）.The three tumor marker values were used as inputs into an ANN and randomized into a training set of 658 （70.31％ were malignant） and a test set of the remaining 255 samples （70.69％ were malignant）.The samples were also evaluated using a Logistic regression （LR） *** The ANN-derived composite index was superior to each of the serum tumor markers alone and the Logistic regression *** areas under receiver operating characteristic curves （AUROC） was 0.905 （95％ confidence Interval （CI） 0.868-0.942） for ANN,0.812 （95％ CI 0.762-0.863） for the Logistic regression model,0.845 （95％ CI 0.798-0.893）for CA19-9,0.795 （95％ CI 0.738-0.851） for CA125,and 0.800 （95％ Cl 0.746-0.854） for *** analysis of multiple markers yielded a high level of diagnostic accuracy （83.53％） compared to LR （74.90％）.Conclusion The performance of ANN model in the diagnosis of pancreatic cancer is better than the single tumor marker and LR model.