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Artificial neural networks approach to early lung cancer detection

  • Research Article
  • Published:
Central European Journal of Medicine

Abstract

Lung cancer is rated with the highest incidence and mortality every year compared with other forms of cancer, therefore early detection and diagnosis is essential. Artificial Neural Networks (ANNs) are “artificial intelligence” software which have been used to assess a few prognostic situations. In this study, a database containing 193 patients from Diagnostic and Monitoring of Tuberculosis and Illness of Lungs Ward in Kuyavia and Pomerania Centre of the Pulmonology (Bydgoszcz, Poland) was analysed using ANNs. Each patient was described using 48 factors (i.e. age, sex, data of patient history, results from medical examinations etc.) and, as an output value, the expected presence of lung cancer was established. All 48 features were retrospectively collected and the database was divided into a training set (n=97), testing set (n=48) and a validating set (n=48). The best prediction score of the ANN model (MLP 48-9-2) was above 0.99 of the area under a receiver operator characteristic (ROC) curve. The ANNs were able to correctly classify 47 out of 48 test cases. These data suggest that Artificial Neural Networks can be used in prognosis of lung cancer and could help the physician in diagnosis of patients with the suspicion of lung cancer.

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References

  1. Jemal A., Siegel R., Xu J., Ward E., Cancer Statistics 2010, Ca. Cancer. J. Clin., 2010, 60, 277–300

    Article  PubMed  Google Scholar 

  2. Siegel R., Naishadham D., Jemal A., Cancer statistics, 2012, CA Cancer J Clin., 2012 62, 10–29

    Article  PubMed  Google Scholar 

  3. Szczuka I., Roszkowski-Ślisz K., Lung cancer in Poland in 1970–2004, Pneumonol. Alergol. Pol., 2008, 76, 19–28

    PubMed  Google Scholar 

  4. Ahmed K., Emran A.A., Jesmin T., Early detection of lung cancer risk using data mining, Asian Pac. J.Cancer Prev., 2013, 14, 595–598

    Article  PubMed  Google Scholar 

  5. Flores J.M., Herrera E., Leal G, Artificial Neural Network-Based Serum Biomarkers Analysis Improves Sensitivity in the Diagnosis of Lung Cancer, IFMBE Proceedings 2013, 33, 882–885

    Article  Google Scholar 

  6. Bishop C.M., Neural networks for pattern recognition, New York, NY: Oxford University Press 1995.

    Google Scholar 

  7. Dayhoff J.E., DeLeo J.M., Artificial neural networks: opening the black box, Cancer, 2001, 91, 1615–1635

    Article  CAS  PubMed  Google Scholar 

  8. Cross S.S., Harrison F.H., Kennedy R.L., Introduction to neural networks. Lancet, 1995, 346, 1075–1079

    Article  CAS  PubMed  Google Scholar 

  9. Baxt W.G., Application of artificial neural networks to clinical medicine, Lancet, 1995, 346, 1135–1138

    Article  CAS  PubMed  Google Scholar 

  10. Amato F., López A., Peña-Méndez E. M., Artificial neural networks in medical diagnosis, J. Appl. Biomed., 2013, 11, 47–58

    Article  CAS  Google Scholar 

  11. Alkim E., Gürbüz E., Kiliç E., A fast and adaptiveautomated disease diagnosis method with an innovative neural network model, Neur. Networks, 2012, 33, 88–96

    Article  Google Scholar 

  12. Atkov O., Gorokhova S., Sboev A., Coronary heart disease diagnosis by artificial neural networks including genetic polymorphisms and clinical parameters, J. Cardiol., 2012, 59, 190–194

    Article  PubMed  Google Scholar 

  13. Buciński A., Bączek T., Kaliszan R., Nasal A., Krysiński J., Załuski J., Artificial Neural Network Analysis of Patient and Treatment Variables as a Prognostic Tool in Breast Cancer after Mastectomy, Adv. Clin. Exp. Med., 2005, 14, 973–979

    Google Scholar 

  14. Patel J.L., Goyal R.K., Application of artificial neural networks in medicinal science, Curr. Clin. Pharmacol., 2007, 2, 217–226

    Article  PubMed  Google Scholar 

  15. Atkov O., Gorokhova S., Sboev A., Coronary heart disease diagnosis by artificial neural networks including genetic polymorphisms and clinical parameters, J. Cardiol., 2012, 59, 190–194

    Article  PubMed  Google Scholar 

  16. Uğuz H., A biomedical system based on artificial neural network and principal component analysis for diagnosis of the heart valve diseases., J. Med. Syst., 2012, 36, 61–72

    Article  PubMed  Google Scholar 

  17. Esteva H., Bellotti M., Marchevsky A.M., Neural networks for the estimation of prognosis in lung cancer, In: Naguib R.N., Sherbet G.V. eds. Artificial neural networks in cancer diagnosis, prognosis and patient management. Boca Raton: CRC Press, 2001: 29–37

    Chapter  Google Scholar 

  18. Bucinski A., Marszall M.P., Krysinski J., Lemieszek A., Zaluski A., Contribution of artificial intelligence to the knowledge of prognostic factors in Hodgkin’s lymphoma, Eur. J. Cancer Prev., 2010, 19, 308–312

    Article  PubMed  Google Scholar 

  19. Cinar M., Engin M., Egin E. Z., Atesci Y. Z., Early prostate cancer diagnosis by using artificial neural networks and support vector machines, Expert Systems with Applications, 2009, 36, 6357–6361

    Article  Google Scholar 

  20. Marszałł M.P., Krysiński J., Sroka W. D., Nyczak Z., Stefanowicz M., Waśniewski T., Romaszko J., Buciński A. ANN as a prognostic tool after treatment of non-seminoma testicular cancer, Central Eur. J. Med. 2012, 7, 672–679

    Article  Google Scholar 

  21. Bączek T., Buciński A., Ivanov A.R., Kaliszan R., Artificial neural network analysis for evaluation of peptide MS/MS spectra in proteomics, Anal. Chem., 2004, 76, 1726–1732

    Article  PubMed  Google Scholar 

  22. Koba M., Application of artificial neural networks for the prediction of antitumor activity of a series of acridinone derivatives, Med. Chem., 2012, 8, 309–319

    Article  CAS  PubMed  Google Scholar 

  23. Luenberger D.G., Ye Y., Linear and nonlinear programming, International Series in Operations Research & Management Science 116 (Third ed.), New York, Springer, 2008, pp. xiv+546

    Google Scholar 

  24. Knyazev, A.V., Lashuk I., Steepest Descent and Conjugate Gradient Methods with Variable Preconditioning, SIAM J. Matrix Anal. A, 2008, 29, 1267–1280

    Article  Google Scholar 

  25. Chen T.M., Kuschner W.G., Non-tobacco related lung carcinogens. Lung cancer principle and practice, In: Harvey P. et al, editors. 3rd ed, Lippincot Williams and Wilkins: 2005. p. 61–73

    Google Scholar 

  26. Bij S., Hendrik Koffijberg H., Lenters V., Lung cancer risk at low cumulative asbestos exposure: meta-regression of the exposure-response relationship, Cancer Cause. Control, 2013, 24, 1–12

    Article  Google Scholar 

  27. Alberg A.J., Samet J.M., Epidemiology of lung cancer, Chest, 2003, 123, 21S–49S

    Article  PubMed  Google Scholar 

  28. Shopland D., Tobacco use and its contribution to early cancer mortality with a special emphasis on cigarette smoking, Environ. Health. Prospect., 1995, 103, 131–142

    Article  Google Scholar 

  29. Doll R., Peto R., Boreham J., Sutherland I., Mortality from cancer in relation to smoking: 50 years observations on British doctors, Br. J. Cancer, 2005, 92, 426–429

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  30. De Matteis S., Consonni D., Pesatori A.C., Are women who smoke at higher risk for lung cancer than men who smoke? Am. J. Epidemiol., 2013, 177, 601–612

    Article  PubMed Central  PubMed  Google Scholar 

  31. Sutedja G., New techniques for early detection of lung cancer, Eur. Respir. J., 2003, 21, 57–66

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Medicinal Chemistry, Collegium Medicum in Bydgoszcz, Nicolaus Copernicus University in Toruń, 85-089, Bydgoszcz, Poland

    Krzysztof Goryński & Michał Piotr Marszałł

  2. Department of Biopharmacy, Collegium Medicum in Bydgoszcz, Nicolaus Copernicus University in Toruń, 85-089, Bydgoszcz, Poland

    Izabela Safian & Adam Buciński

  3. Ward of Diagnostic-monitoring of Tuberculosis and Illness of Lungs, Voivodship Centre of the pulmonology, 85-326, Bydgoszcz, Poland

    Włodzimierz Grądzki

  4. Department of Pharmaceutical Technology, Collegium Medicum in Bydgoszcz, Nicolaus Copernicus University in Toruń, 85-089, Bydgoszcz, Poland

    Jerzy Krysiński

  5. Department of Paliative Medicine, Regional Specialist Hospital in Grudziadz, 86-300, Grudziadz, Poland

    Sławomir Goryński

  6. Chair and Department of Hygiene and Epidemiology, Collegium Medicum in Bydgoszcz, Nicolaus Copernicus University in Toruń, 85-094, Bydgoszcz, Poland

    Anna Bitner

  7. NZOZ Pantamed Sp z o.o. in Olsztyn, ul. Pana Tadeusza 6, 10-461, Olsztyn, Poland

    Jerzy Romaszko

Authors
  1. Krzysztof Goryński
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  2. Izabela Safian
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  3. Włodzimierz Grądzki
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  4. Michał Piotr Marszałł
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  5. Jerzy Krysiński
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  6. Sławomir Goryński
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  7. Anna Bitner
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  8. Jerzy Romaszko
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  9. Adam Buciński
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Correspondence to Krzysztof Goryński.

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Goryński, K., Safian, I., Grądzki, W. et al. Artificial neural networks approach to early lung cancer detection. cent.eur.j.med 9, 632–641 (2014). https://doi.org/10.2478/s11536-013-0327-6

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  • DOI: https://doi.org/10.2478/s11536-013-0327-6

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