Value of combined determination of tumor markers based on two discriminative models in facilitating diagnosis of hepatic carcinoma
- Author:
Xue-Feng BAI
1
Author Information
1. Department of General Surgery, 159 Hospital of PLA
- Publication Type:Journal Article
- Keywords:
Computer-assisted;
Diagnosis;
Liver neoplasms;
Neural networks (computer)
- From:
Medical Journal of Chinese People's Liberation Army
2012;37(11):849-852
- CountryChina
- Language:Chinese
-
Abstract:
Objective To explore the value of determination of combined tumor markers based on artificial neural network (ANN) discrimination model in facilitating the diagnosis of hepatic carcinoma. Methods Serum samples were collected from three groups of subjects, including 50 cases of liver cancer, 40 cases of benign liver disease, and 50 normal controls. The levels of serum alpha fetoprotein (AFP), carbohydrate antigen 125 (CA125) and carcino-embryonic antigen (CEA) were determined by chemiluminescence immunoassay. The level of serum sialic acid (SA) was determined by spectrophotometry, the content of calcium in serum was measured by calcium assay kit (Azo-end method of arsenic HI). Based on the five tumor markers mentioned above as discrimination variables, Fisher discrimination and ANN were applied to set up the intelligent auxiliary diagnostic model. Results By applying the Fisher discrimination model established in present work, the diagnostic sensitivity of liver cancer was 46.1%, the specificity was 98.9%, the accurate rate was 79.3%, the positive predictive value was 95.8%, and the negative predictive value was 76.7% for the three groups. With the application of ANN discrimination model, the diagnostic sensitivity of liver cancer was raised to 96.0%, the specificity 98.9%, the accuracy 94.3%, the positive predictive value 98.0%, and the negative predictive value was 97.8%. Conclusion The diagnostic model based on ANN combined with 5 tumor markers is superior in diagnostic acuity to traditional Fisher discrimination analysis, thus more suitable for clinical data analysis.
