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

• Published in: Chinese medical journal Vol. 127; no. 10; pp. 1891 - 1896
• Main Authors: Yang, Yingchi, Chen, Hui, Wang, Dong, Luo, Wei, Zhu, Biyun, Zhang, Zhongtao
• Format: Journal Article
• Language: English
• Published: China Department of General Surgery, Beijing Friendship Hospital, Capital Medical University, Beijing 100050, China%Institute of Biomedical Engineering, Capital Medical University, Beijing 100069, China 20-05-2014
• Subjects: Adult; Aged; Aged, 80 and over; ANN模型; Biomarkers, Tumor - blood; CA-125 Antigen - blood; CA-19-9 Antigen; Female; Humans; Logistic Models; Logistic回归模型; Male; Middle Aged; Neural Networks, Computer; Pancreatic Neoplasms; Pancreatic Neoplasms - blood; Retrospective Studies; 人工神经网络模型; 检测诊断; 神经网络分析; 肿瘤标志物; 胰腺癌; 血清; artificial neural network; Logistic regression; pancreatic cancer; tumor markers
• ISSN: 0366-6999; 2542-5641
• DOI: 10.3760/cma.j.issn.0366-6999.20133101

Background Artificial neural network （ANN） has demonstrated the ability to assimilate information from multiple sources to enable the detection of subtle and complex patterns.In this reseamh,we evaluated an ANN model in the diagnosis of pancreatic cancer using multiple serum markers.Methods 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） model.Results The ANN-derived composite index was superior to each of the serum tumor markers alone and the Logistic regression model.The 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 CEA.ANN 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.