Name
Title | ||
|---|---|---|
An optimal tumor marker group-coupled artificial neural network for diagnosis of lung cancer |
Abstract | ||
|---|---|---|
Background: Epidemiological statistics has shown that there are approximately 1.2 million new cases of lung cancer diagnosed every year and the death rate of these patients is 17.8%. Earlier diagnosis is key to promote the five-year survival rate of these cancer patients. Some tumor markers have been found to be valuable for earlier diagnosis, but a single marker has limitation in its sensitivity and specificity of cancer diagnosis. To improve the efficiency of diagnosis, several distinct tumor marker groups are combined together using a mathematical evaluation model, called artificial neural network (ANN). Lung cancer markers have been identified to include carcinoembryonic antigen, carcinoma antigen 125, neuron specific enolase, @b"2-microglobulin, gastrin, soluble interleukin-6 receptor, sialic acid, pseudouridine, nitric oxide, and some metal ions. Methods: These tumor markers were measured through distinct experimental procedures in 50 patients with lung cancer, 40 patients with benign lung diseases, and 50 cases for a normal control group. The most valuable were selected into an optimal tumor marker group by multiple logistic regression analysis. The optimal marker group-coupled ANN model was employed as an intelligent diagnosis system. Results: We have presented evidence that this system is superior to a traditional statistical method, its diagnosis specificity significantly improved from 72.0% to 100.0% and its accuracy increased from 71.4% to 92.8%. Conclusions: The ANN-based system may provide a rapid and accurate diagnosis tool for lung cancer. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1016/j.eswa.2011.02.183 | Expert Syst. Appl. |
Keywords | Field | DocType |
lung cancer,tumor marker,artificial neural network,intelligent diagnosis system,cancer patient,diagnosis specificity,accurate diagnosis tool,benign lung disease,diagnosis,cancer diagnosis,lung cancer marker,optimal tumor marker,earlier diagnosis,metal ion,nitric oxide,neuron specific enolase,beta 2 microglobulin | Lung cancer,Lung,Antigen,Computer science,Carcinoembryonic antigen,Artificial intelligence,Tumor marker,Oncology,Survival rate,Internal medicine,Machine learning,Carcinoma,Cancer | Journal |
Volume | Issue | ISSN |
38 | 9 | Expert Systems With Applications |
Citations | PageRank | References |
6 | 0.70 | 0 |
Authors | ||
7 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yongjun Wu | 1 | 6 | 0.70 |
| Yiming Wu | 2 | 6 | 0.70 |
| Jing Wang | 3 | 6 | 0.70 |
| Zhen Yan | 4 | 6 | 0.70 |
| Lingbo Qu | 5 | 9 | 1.40 |
| Bingren Xiang | 6 | 9 | 1.40 |
| Yiguo Zhang | 7 | 20 | 1.77 |