The effect of artificial neural network model combined with six tumor markers in auxiliary diagnosis of lung cancer

Journal of Medical Systems

Volumn 36, Issue 5, 2012, Pages 2973-2980

  Feng, Feifei ^a   Wu, Yiming ^a   Wu, Yongjun ^a   Nie, Guangjin ^a   Ni, Ran ^b  

^a ZHENGZHOU UNIVERSITY   (China)

^b THE FIRST AFFILIATED HOSPITAL OF ZHENGZHOU UNIVERSITY   (China)

Author keywords

Artificial neural network; Gastrointestinal cancer; Lung cancer; Tumor marker

Indexed keywords

CARCINOEMBRYONIC ANTIGEN; CUPROUS ION; FETAL ANTIGEN; GASTRIN; NEURON SPECIFIC ENOLASE; SIALIC ACID; ZINC; TUMOR MARKER;

ADULT; ARTICLE; ARTIFICIAL NEURAL NETWORK; CANCER PATIENT; COLON CANCER; CONTROLLED STUDY; ESOPHAGUS CANCER; FEMALE; HUMAN; LUNG CANCER; MACHINE LEARNING; MAJOR CLINICAL STUDY; MALE; SENSITIVITY AND SPECIFICITY; STOMACH CANCER; ZINC BLOOD LEVEL; ALGORITHM; BLOOD; COMPUTER ASSISTED DIAGNOSIS; COMPUTER SIMULATION; DIFFERENTIAL DIAGNOSIS; GASTROINTESTINAL TUMOR; LUNG TUMOR; METHODOLOGY; PATHOLOGY;

ALGORITHMS; COMPUTER SIMULATION; DIAGNOSIS, COMPUTER-ASSISTED; DIAGNOSIS, DIFFERENTIAL; GASTROINTESTINAL NEOPLASMS; HUMANS; LUNG NEOPLASMS; NEURAL NETWORKS (COMPUTER); SENSITIVITY AND SPECIFICITY; TUMOR MARKERS, BIOLOGICAL;

EID: 84867291894     PISSN: 01485598     EISSN: 1573689X     Source Type: Journal    
DOI: 10.1007/s10916-011-9775-1     Document Type: Article

References (25)

1. Jemal, A.; Siegel, R.; Ward, E.; Hao, Y.; Xu, J.; Thun, M. J.; Cancer statistics. CA Cancer J. Clin. 59:225-249, 2009.
2. Ghosal, R.; Kloer, P.; Lewis, K. E.; A review of novel biological tools used in screening for the early detection of lung cancer. Postgrad. Med. J. 85:358-363, 2009.
3. Greenberg, A. K.; Lee, M. S.; Biomarkers for lung cancer: clinical uses. Curr. Opin. Pulm. Med. 134:249-255, 2007.
4. Ma, P. C.; Blaszkowsky, L.; Bharti, A.; Ladanyi, A.; Kraeft, S. K.; Bruno, A.; Skarin, A. T.; Chen, L. B.; Salgia, R.; Circulating tumor cells and serum tumor biomarkers in small cell lung cancer. Anticancer. Res. 23:49-62, 2003.
5. Chu, X. Y.; Hou, X. B.; Song, W. A.; Xue, Z. Q.; Wang, B.; Zhang, L. B.; Diagnostic values of SCC, CEA, Cyfra21-1 and NSE for lung cancer in patients with suspicious pulmonary masses: a single center analysis. Canc. Biol. Ther. 11, 2011.
6. Niho, S.; Shinkai, T.; Tumor markers in lung cancer. Gan To Kagaku Ryoho/Canc. Chemother. 28:2089-2093, 2001.
7. Giovanella, L.; Ceriani, L.; Bandera, M.; Garancini, S.; Immunoradiometric assay of chromogranin A in the diagnosis of small cell lung cancer: comparative evaluation with neuron-specific enolase. Int. J. Biol. Markers. 16:50-55, 2001.
8. Chao, C.; Hellmich, M. R.; Gastrin, inflammation, and carcinogenesis. Curr. Opin. Endocrinol. Diabetes. Obes. 17:33-39, 2010.
9. de Castro, J.; Rodríguez, M. C.; Martínez-Zorzano, V. S.; Hernández-Hernández, A.; Llanillo, M.; Sánchez-Yagüe, J.; Erythrocyte and platelet phospholipid fatty acids as markers of advanced non-small cell lung cancer: comparison with serum levels of sialic acid, TPS and Cyfra 21-1. Canc. Investig. 26:407-418, 2008.
10. Dou, L.; Xu, W.; Determination of 14 elements in the body fluid and hair of lung cancer patients by microwave digestion with ICP-MS. Chin. J. Lung Canc. 13:817-820, 2010.
11. Caron, J.; Mangé, A.; Guillot, B.; Solassol, J.; Highly sensitive detection of melanoma based on serum proteomic profiling. J. Canc. Res. Clin. Oncol. 135:1257-1264, 2009.
12. Cartwright, H. M.; Artificial neural networks in biology and chemistry: the evolution of a new analytical tool. Meth. Mol. Biol. 458:1-13, 2008.
13. Mat-Isa, N. A.; Mashor, M. Y.; Othman, N. H.; An automated cervical pre-cancerous diagnostic system. Artif. Intell. Med. 42:1-11, 2008.
14. Bassi, P.; Sacco, E.; De Marco, V.; Aragona, M.; Volpe, A.; Prognostic accuracy of an artificial neural network in patients undergoing radical cystectomy for bladder cancer: a comparison with logistic regression analysis. Brit. J. Urol. Int. 99:1007-1012, 2007.
15. Zhang, Z.; Yu, Y.; Xu, F.; Berchuck, A.; van Haaften-Day, C.; Havrilesky, L. J.; de Bruijn, H. W.; van der Zee, A. G.; Woolas, R. P.; Jacobs, I. J.; Skates, S.; Chan, D. W.; Bast, R. C.; Jr.; Combining multiple serum tumor markers improves detection of stage I epithelial ovarian cancer. Gynecol. Oncol. 107:526-531, 2007.
16. Feng, F. F.; Wu, Y. J.; Zhang, C.; Wu, Y. M.; Application of artificial neural network model established by tumor markers and bronchofibroscopic data in auxiliary diagnosis of lung cancer. Nat. Comput. 2:118-125, 2009.
17. Wu, Y. J.; Hao, Y. H.; Wu, W. C.; Wu, Y. M.; Value of auto-fluorescence spectrum combined with tumor markers in diagnosis of lung cancer. Spectrosc. Spect. Anal. 29:2787-2791, 2009.
18. Wu, Y. J.; Wu, Y. M.; Wang, J.; Yan, Z.; Qu, L. B.; Xiang, B. R.; Zhang, Y. G.; An optimal marker group-coupled artificial neural network for diagnosis of lung cancer. Expert Syst. Appl. 38:11329-11334, 2011.
19. Wu, M. T.; Lee, L. H.; Ho, C. K.; Wu, S. C.; Lin, L. Y.; Cheng, B. H.; Liu, C. L.; Yang, C. Y.; Tsai, H. T.; Wu, T. N.; Environmental exposure to cooking oil fumes and cervical intraepithelial neoplasm. Environ. Res. 4:25-32, 2004.
20. Li, M.; Yin, Z.; Guan, P.; Li, X.; Cui, Z.; Zhang, J.; Bai, W.; He, Q.; Zhou, B.; XRCC1 polymorphisms, cooking oil fume and lung cancer in Chinese women nonsmokers. Lung Canc. 62:145-151, 2008.
21. Yu, J. K.; Yang, M. Q.; Jiang, T. J.; Zheng, S.; The optimal combination of serum tumor markers with bioinformatics in diagnosis of colorectal carcinoma. J. Zhejiang. Univ. Med. Sci. 33:407-410, 2004.
22. Donacn, M.; Yu, Y.; Artioli, G.; Banna, G.; Feng, W.; Bast, R. C.; Jr.; Zhang, Z.; Nicoletto, M. O.; Combined use of biomarkers for detection of ovarian cancer in high-risk women. Tumor Biol. 31:209-215, 2010.
23. Patel, J. L.; Goyal, R. K.; Applications of artificial neural networks in medical science. Curr. Clin. Pharmacol. 2:217-226, 2007.
24. Bloom, G. C.; Eschrich, S.; Zhou, J. X.; Coppola, D.; Yeatman, T. J.; Elucidation of a protein signature discriminating six common types of adenocarcinoma. Int. J. Canc. 120:769-775, 2007.
25. Liu, B.; Cui, Q.; Jiang, T.; Ma, S.; A combinational feature selection and ensemble neural network method for classification of gene expression data. BMC Bioinforma. 5:136, 2004.