ORHAN ER, A. Çetin TANRIKULU, Abdurrahman ABAKAY

Use of artifcial intelligence techniques for diagnosis of malignant pleural mesothelioma

Amaç: İnsanların beyin zarında bulunan, asbestos ve asbestiform liferine maruz kalmakla oluşan kötü huylu plevral Mezotelyoma, oldukça saldırgan bir tümördür. Düşük seviyeli çevresel erionite fibrous zeolitee maruz bırakılmış Türkiyedeki bazı kasabalarda Mezotelyoma görülme oranı oldukça yüksektir. Yöntemler: Bu çalışmada Mezotelyoma hastalığı teşhisi yapay bağışıklık sistemi kullanımı ile gerçekleştirilmiştir. Bununla beraber yapay bağışıklık sistemi sonuçları, aynı veri tabanını kullanan, Mezotelyoma hastalığının teşhisine odaklanmış çok katmanlı yapay sinir ağı sonuçları ile karşılaştırılmıştır. Mezotelyoma hastalığı veri seti, hastaların hastane raporlarını kullanan tıp fakültesi veri tabanından alınmıştır. Bulgular: Yapay bağışıklık sistemi tarafından hastalık teşhisi için %97,74 doğruluk oranında bir performans elde edilmiştir. Yapay bağışıklık sistemi algoritmasının doğruluk sonuçları çok katmanlı yapay sinir ağı algoritmasından çok daha iyi olduğu görülmüştür. Sonuç: Bu sistem uzmana, sağlıklı ve hasta kişiyi sınıflandırma sürecinde doğru teşhisi bulma yönünde iyi bir performans sağlar. Böylece bu yapı ile doğru teşhis sonucuna ulaşmada doktorlara bir karar destek sistemi olarak yardımcı olur.

Malign plevral mezotelyoma tanısı için yapay zeka teknikleri kullanımı

Objective: Malignant pleural mesothelioma is a highly aggressive tumor of the serous membranes, which in humans results from exposure to asbestos and asbestiform fbers. The incidence of malignant mesothelioma is extremely high in some Turkish villages where there is a low-level environmental exposure to erionite, a fbrous zeolite. Therefore epidemiological studies are diffcult to perform in Turkey. Methods: In this paper, a study on malignant pleural mesothelioma disease diagnosis was realized by using artifcial immune system. Also, the artifcial immune system result was compared with the result of the multi-layer neural network focusing on malignant pleural mesothelioma disease diagnosis and using same database. The malignant pleural mesothelioma disease dataset were prepared from a faculty of medicines database using patients hospital reports. Results: 97.74% accuracy performance is obtained by artifcial immune system. The accuracy results of artifcial immune system algorithm are much better than the accuracy results of multi-layer neural network algorithm. Conclusion: This system is capable of conducting the classifcation process with a good performance to help the expert while deciding the healthy and patient subjects. So, this structure can be helpful as learning based decision support system for contributing to the doctors in their diagnosis decisions.

PDF

___

1. Wagner JC, Sleggs CA, Marchand P. Diffuse pleural meso - thelioma and asbestos exposure in the North Western Cape Povince. Br J Indust Med 1960;17:266-271.
2. Emri S, Akbulut H, Zorlu F, et al. Prognostic signifcance of fow cytometric DNA analysis in patients with malignant pleural mesothelioma. Lung Cancer 2001; 33:109-114.
3. Barış B, Demir AU, Shehu V, et al. Environmental fbrous zeolite (erionite) exposure and malignant tumors other than mesothelioma. J Environ Pathol Toxicol Oncol 1996; 15:183-189.
4. Dumortier P, Çöplü L, De Maertelaer V, et al. Assessment of environmental asbestos exposure in Turkey by bronchoal- veolar lavage. Am J Respir Crit Care Med 1998; 158:1815- 24.
5. Selcuk ZT, Coplu L, Emri S, et al. Malignant pleural me - sothelioma due to environmental mineral fber exposure in Turkey. Analysis of 135 cases. Chest 1992;102:790-796.
6. Peto J, Decarli A, La Vecchia C, et al. The European meso - thelioma epidemic Brit J Cancer 1999;79:666-672.
7. Burgers JA, Damhuis RAM, Prognostic factors in malignant mesothelioma Lung Cancer 2004;56:123-129.
8. Zervos MD, Bizekis C. Pass HI: Malignant mesothelioma 2008. Curr Opin Pulm Med 2008, 14:303-309.
9. National Mesothelioma committee, http://www.mesothelio - ma-tr.org/ (last accepted: 10 November 2014)
10. Tanrikulu AC, Senyigit A, Dagli CE, et al. Environmental malignant pleural mesothelioma in Southeast Turkey. Saudi Med J 2006;27:1605-1607.
11. Senyiğit A, Bayram H, Babayiğit C, et al. Malignant pleural mesothelioma caused by environmental exposure to asbes - tos in the Southeast of Turkey: CT fndings in 117 patients. Respiration 2000;67:615-622.
12. Senyiğit A, Babayiğit C, Gökirmak M, et al. Incidence of malignant pleural mesothelioma due to environmental as - bestos fber exposure in the southeast of Turkey. Respiration 2000;67:610-614.
13. Can N, Puyan FÖ, Öz F, et al. Warthin-like papillary thyroid carcinoma: A rare tumor of the thyroid. Dicle Medical Journal 2011;38 :482-485.
14. Alberts AS, Faikson G, Goedhals L, et al. Malignant pleural mesothelioma: a disease unaffected by current therapeutic maneuvers. J. Clin Oncol 1988;6:527-535.
15. Vogelzang NJ. Malignant mesothelioma: diagnostic and management strategies for 1992. Semin Oncol 1992;19:64- 71.
16. Chailleux E, Dabouis G, Pioche D, et al. Prognostic factors in diffuse malignant pleural mesothelioma. Chest 1988;93:159-162.
17. Schouwink H, Korse CM, Bonfrer JM, et al. Prognostic value of the serum tumour markers Cyfra 21-1 and tissue polypeptide antigen in malignant mesothelioma. Lung Cancer 1999;25:25-32.
18. Sugarbaker D.J., Jaklitseh M.T., Liptay M.J., Mesothelioma and radical multimodality therapy: who benefts? Chest 1995; 107(Suppl.): 3455-3505.
19. Sugarbaker D.J., Norberto J.J., Multimodality management of malignant pleural mesothelioma. Chest 1998; 113 (Sup- pl.): 615 -655.
20. Metintas M, Metintas S, Ucgun I, et al. Prognostic factors in diffuse malignant pleural mesothelioma: pretreatment clinical and laboratory characteristics. Resp. Medicine 2001: 95:829-835.
21. Edwards JG, Abrams KR, Leverment JN. Prognostic factors for malignant mesothelioma in 142 patients: validation of CALGB and EORTC prognostic scoring systems. Thorax 2000;55:731-735.
22. Curran D, Sahmoud T, Therasse P. Prognostic factors in patients with pleural mesothelioma: the European Organisa¬tion for research and treatment of cancer experience. J Clin OncoI 1998;16:145-152.
23. Montanaro F, Rosato R, Gangemi M, et al. Survival of pleural malignant mesothelioma in Italy: a population-based study. Int J Cancer. 2009;124:194-200.
24. Kadoz H, Ozsen S, Arslan A and Gunes S. Medical applica - tion of information gain based artifcial immune recognition system (AIRS): Diagnosis of thyroid disease. Expert System with Application. 2008;36:3086-3092.
25. Engin O, Döyen A. Artifcial Immune Systems and Applications in Industrial Problems. GU J Sci 2004;17:71-84.
26. Dasgupta D. Advances in Artifcial Immune Systems. EEE Computational Intelligence Magazine, University of Mem - phis, USA, November 2006; 40-49.
27. Kayaer K, Yıldırım T. Medical Diagnosis on Pima Indian Diabetes Using General Regression Neural Networks. In Proc. of International Conference on Artifcial Neural Net- works and Neural Information Processing (ICANN/ICO - NIP), Istanbul, 2003; 181-184.
28. Delen D, Walker G, Kadam A., Predicting breast cancer survivability: A comparison of three data mining methods, Artifcial Intelligence in Medicine Artifcial Intelligence in Medicine, 2005; 34: 113127.
29. Temurtas F. A comparative study on thyroid disease diagnosis using neural networks. Expert Systems with Applications, 2009; 36: 944-949.
30. Rumelhart DE, Hinton GE, Williams RJ. Learning internal representations by error propagation, in D.E. Rumelhart, J.L. McClelland (Eds.), Parallel Distributed Processing: Explorations in the Microstructure of Cognition, 1986; 1: 318362.
31. Brent RP. Fast training algorithms for multi-layer neural nets. IEEE Trans. Neural Networks, 1991; 2: 346354.
32. Gori M, Tesi A. On the problem of local minima in back- propagation. IEEE Trans. Pattern Anal. Machine Intell. 1992; 14: 7685.
33. Hagan MT, Menhaj M. Training feed forward networks with the Marquardt algorithm. IEEE Trans. Neural Net- works, 1994; 5: 989-993.
34. Hagan MT, Demuth HB, Beale MH. Neural Network Design. PWS Publishing, Boston, MA, 1996.
35. Gulbag A, Temurtas F. A study on quantitative classifcation of binary gas mixture using neural networks and adaptive neuro fuzzy inference systems. Sensor Actuators B, 2006; 115: 252-262.
36. Trojanowski K, Wierzchon ST. Searching for Memory in Artifcial Immune System. The Eleventh International Symposium on Intelligent Information Systems, June 3-6, 2002.
37. De Castro LN and Timmis J. A Novel approach to pattern recognition. Artifcial Neural Networks in pattern Recognition, University of Paisley: 2002; 67-84.
38. De Castro LN and Von Zuben FJ. The clonal selection algorithm with engineering applications. GECCO 2000, Las Vegas, Nevada, USA, July 8, 2000.
39. Er O, Sertkaya C, Temurtas F and Tanrikulu AC. A Comparative study on chronic obstructive pulmonary and pneu - monia diseases diagnosis using neural networks and artifcial immune system. Journal of Medical Systems, 2009; 33: 485-492.
40. Watkins A. AIRS: A resource limited artifcial immune clas - sifer. Master Thesis, Mississippi State University, 2001.
41. Sertkaya C. Immune Base System in Computer Security, Master Thesis. Sakarya University. Institute of Science and Technology, 2009.
42. Er O, Yumusak N, Temurtas F. Chest diseases diagnosis using artifcial neural networks. Expert Systems with Applications, 2010; 37: 7648-7655.
43. Ozyılmaz L, Yıldırım T. Diagnosis of thyroid disease using artifcial neural network methods. In Proc. of ICONIP02 9th international conference on neural information processing, Orchid Country Club, Singapore, 2002; 20332036.
44. Er O, Temurtas F. A study on chronic obstructive pulmonary disease diagnosis using multilayer neural networks. Journal of Medical Systems, 2008; 32: 429-432.
45. Er O, Temurtas F, Tanrikulu AC. Tuberculosis disease diagnosis using artifcial neural networks. J Med Systems 2010;34:299-302.
46. Er O, Tanrikulu AC, Abakay A, Temurtas F. An approach based on probabilistic neural network for diagnosis of Me - sotheliomas disease. Computers & Electrical Engineering 2011;38:75-81.