A nonlinear model for diagnosing malignancy in patients with exudative plural effusion using routine plural fluid findings

Abstract

Background: There is a challenge in diagnosing cancer in patients with exudative plural effusion using a noninvasive and accurate method.

Objective: We developed artificial neural network (ANN), as a nonlinear model, to discriminate malignant exudative plural effusion from nonmalignant based on routine pleural fluid findings.

Methods: The plural fluid parameters including total and differential cell counts, total proteins, lactate dehydrogenase (LDH), glucose, adenosine deaminase (ADA), as well as age and sex of 114 patients with exudative plural effusion were applied by models as input. The output was supposed to be the presence or absence of the cancer.

Results: The accuracy, sensitivity and specificity of ANN for predicting malignancy were 89.7%, 86.7%, and 91.7%, respectively. In addition, the neural network significantly outperformed the logistic regression model, as a linear model, (AUC: 0.892 vs. 0.633, respectively, p < 0.001).

Conclusion: The ANN is a novel accurate and noninvasive method that can be used clinically to diagnose malignancy in patients with exudative plural effusion.

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  • 1. Marel M, Zrustova M, Stasny B, Light RW. The incidence of pleural effusion in a well-defined region. Epidemiologic study in central Bohemia. Chest. 1993; 104:1486-9.

  • 2. Villena V, Lopez Encuentra A, Echave-Sustaeta J, Alvarez-Martinez C, Martin-Escribano P. Prospective study of 1,000 consecutive patients with pleural effusion. Etiology of the effusion and characteristics of the patients. Arch Bronconeumol. 2002; 38:21e6.

  • 3. Porcel-Perez JM, Vives-Soto M, Esquerda-Serrano A, Jover-Saenz A. Cuttoff values of biochemical tests on pleural fluid: their usefulness in differential diagnosis of 1,040 patients with pleural effusion. An Med Interna (Madrid). 2004; 21:113e7.

  • 4. Valdes L, Pose A, SanJose E, Martinez Vasquez JM. Tuberculous pleural effusions. Eur J Intern Med. 2003; 14:77-88

  • 5. Frank W. Tuberculous pleural effusions. Eur Respir Mon. 2002; 22:219-33

  • 6. Antonangelo L, Vargas FS, Seiscento M, Bombarda S, Teixeira L, Sales RKB. Clinical and laboratory parameters in the differential diagnosis of pleural effusion secondary to tuberculosis or cancer. Clinics. 2007; 62:585-90.

  • 7. Valdes L, Alvarez D, San Jose E, Penela P, Valle JM, Garcia-Pazos JM, et al. Tuberculous pleurisy: a study of 254 patients. Arch Intern Med. 1998; 158:2017-21.

  • 8. Escudero Bueno C, Garcia Clemente M, Cuesta Castro B, Molinos Martin L, Rodriguez Ramos S, Gonzalez Panizo A, et al. Cytologic and bacteriologic analysis of fluid and pleural biopsy specimens with Cope’s needle: study of 414 patients. Arch Intern Med. 1990; 150:1190-4.

  • 9. Trajman A, Pai M, Dheda K, van Zyl Smit R, Zwerling AA, Joshi R, et al. Novel tests for diagnosing tuberculous pleural effusion: what works and what does not? Eur Respir J. 2008; 31:1098-106.

  • 10. Maskell NA, Butland RJ. Pleural diseases group, standards of care committee, British Thoracic Society. BTS guidelines for the investigation of a unilateral pleural effusion in adults. Thorax. 2003; 58(Suppl.2): ii8e17.

  • 11. Daniil ZD, Zintzaras E, Kiropoulos T, Papaioannou AI, Koutsokera A, Kastanis A, et al. Discrimination of exudative pleural effusions based on multiple biological parameters. Eur Respir J. 2007; 30:957-64.

  • 12. Korczynski P, Krenke R, Safianowska A. Diagnostic utility of pleural fluid and serum markers in differentiation between malignant and non-malignant pleural effusions. Eur J Med Res. 2009; 14 Suppl 4: 128-33.

  • 13. Rodriguez-Panadero F, Janssen JP, Astoul P. Thoracoscopy: general overview and place in the diagnosis and management of pleural effusion. Eur Respir J. 2006; 28:409-21.

  • 14. Raoufy MR, Vahdani P, Alavian SM, Fekri S, Eftekhari P, Gharibzadeh S. A novel method for diagnosing cirrhosis in patients with chronic hepatitis B: artificial neural network approach. J Med Syst. 2011; 35:121-6.

  • 15. Raoufy MR, Eftekhari P, Gharibzadeh S, Masjedi MR. Predicting arterial blood gas values from venous samples in patients with acute exacerbation chronic obstructive pulmonary disease using artificial neural network. J Med Syst. 2011; 35:483-8.

  • 16. Jang JSR. Self-learning fuzzy controllers based on temporal backpropagation. IEEE Trans Neural Netw. 1992; 3:714-23.

  • 17. Tu JV. Advantages and disadvantages of using artificial neural networks versus logistic regression for predicting medical outcomes. J Clin Epidemiol. 1996; 49:1225-31.

  • 18. Rodriguez-Panadero F, Janssen JP, Astoul P. Thoracoscopy: general overview and place in the diagnosis and management of pleural effusion. Eur Respir J. 2006; 28:409-22.

  • 19. Sahn SA, Good Jr JT. Pleural fluid pH in malignant effusions. Diagnostic, prognostic, and therapeutic implications. Ann Intern Med. 1988; 108:345-9.

  • 20. Antony VB, Loddenkemper R, Astoul P. Management of malignant pleural effusions. Am J Respir Crit Care Med. 2000; 162:1987-2001.

  • 21. Antunes G, Neville E, Duffy J, Ali N. BTS guidelines for the management of malignant pleural effusions. Thorax. 2003; 58:Suppl 2, ii29-ii38.

  • 22. Boutin C, Viallat JR, Cargnino P, Farisse P. Thoracoscopy in malignant pleural effusions. Am Rev Respir Dis. 1981; 124:588-92.

  • 23. Davidson AC, George RJ, Sheldon CD, Sinha G, Corrin B, Geddes DM. Thoracoscopy: assessment of a physician service and comparison of a flexible bronchoscope used as a thoracoscope with a rigid thoracoscope. Thorax. 1988; 43:327-32.

  • 24. Menzies R, Charbonneau M. Thoracoscopy for the diagnosis of pleural disease. Annals Int Med. 1991; 114:271-6.

  • 25. Enk B, Viskum K. Diagnostic thoracoscopy. Eur J Resp Dis. 1981; 62:344-51.

  • 26. DeCamp PT, Mosely PW, Scott ML. Diagnostic thoracoscopy. Ann Thorac Surg. 1973; 16:79-84.

  • 27. Swets JA. Measuring the accuracy of diagnostic systems. Science. 1988; 240:1285-93.

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