Forecasting of Survival Rate in Patients with the Early Stage of Non Small Cell Lung Cancer

Abstract

Lung cancer is the most common cause of death from oncological diseases all over the world. Primary treatment of patients with the early stage of non-small cell lung cancer is a surgery. However, after surgery 30% - 85% of patients undergo disease progression. In order to improve the results of treatment of patients with non-small cell lung cancer it is necessary to separate a group of patients with dismal prognosis for whom adjuvant chemotherapy will permit improving the survival rate. The aim of our research was to create a forecasting model with a view to detect the patients with the early stage of non-small cell lung cancer and dismal prognosis. Our research covered 254 patients with the early stage of non-small cell lung cancer who underwent a cure from June 2008 till December 2012 in the department of thoracic surgery of Zaporizhzhia Regional Clinical Oncologic Dispensary. In order to identify the factors connected with the risks of low survival rate of patients with the early stage of non-small cell lung cancer after curative treatment (surgical treatment, adjuvant chemotherapy), a method of design of neural network models of classification was used. 39 factors were taken for input characteristics. During investigation two forecasting models were built. As follows from the analysis of first forecasting model with the increase of the patient’s BMI, the risk of low patient survival rate statistically and significantly (p = 0.03) decreases, OR = 0.89 (95% CI 0.80 - 0.99) for each kg/m2 index value. The risk of low patient survival rate also decreases (p = 0.02) if he has a squamous cell carcinoma, OR = 0.36 (95% CI 0.15 - 0.88) compared with other histological forms of tumor. The connection between the risk of low patient survival rate and the volume of surgical intervention was discovered (p = 0.01), OR = 3.19 (95% CI 1.29 - 7.86) for patients who underwent a pulmonectomy compared with patients who underwent an upper bilobectomy. As follows from the analysis of second forecasting model with the increase of the patient’s BMI the risk of low patient survival rate statistically and significantly (p = 0.01) decreases; OR = 0.84 (95% CI 0.74 - 0.96) for each kg/m2 index value. It is found that with the increasing level of EGFR expression in the primary tumor, the risk of low patient survival rate statistically and significantly increases (p = 0.04), OR = 1.39 (95% CI 1.01 - 1.90) for each graduation rate. The risk of low patient survival rate also increases when conducting the lymph dissection in the volume D0 - D1.

Share and Cite:

O. Kolesnik, A. Shevchenko, Y. Lyakh and V. Gurianov, "Forecasting of Survival Rate in Patients with the Early Stage of Non Small Cell Lung Cancer," Journal of Cancer Therapy, Vol. 4 No. 10, 2013, pp. 1472-1477. doi: 10.4236/jct.2013.410177.

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1] D. T. Chen, Y.-L. Hsu, W. J. Fulp, et al., “Prognostic and Predictive Value of a Malignancy-Risk Gene Signature in Early-Stage Non-Small Cell Lung Cancer,” Journal of the National Cancer Institute, Vol. 103, No. 24, 2011, pp. 1859-1870. http://dx.doi.org/10.1093/jnci/djr420
[2] T. A. D’Amico, M. Massey, J. E. Herndon, et al., “A Biologic Risk Model for Stage I Lung Cancer: Immunohistochemical Analysis of 408 Patients with the Use of Ten Molecular Markers,” The Journal of Thoracic and Cardiovascular Surgery, Vol. 117, No. 4, 1999, pp. 736-743. http://dx.doi.org/10.1016/ S0022-5223(99)70294-1
[3] K. Konopa, “Do We Have Markers to Select Patients for Adjuvant Therapies of Non-Small-Cell Lung Cancer?” Annals of Oncology, Vol. 21, No. 7, 2010, pp. 199-202.
[4] S. Y. Park, H. S. Lee, H. J. Jang, et al., “Tumor Necrosis as a Prognostic Factor for Stage IA Non-Small Cell Lung Cancer,” The Annals of Thoracic Surgery, Vol. 91, No. 6, 2011, pp. 1668-1673.
http://dx.doi.org/10.1016/j.athoracsur.2010.12.028
[5] B. A. Williams, H. Sugimura, Ch. Endo, et al., “Predicting Postrecurrence Survival Among Completely Resected Nonsmall-Cell Lung Cancer Patients,” The Annals of Thoracic Surgery, Vol. 81, No. 3, 2006, pp. 1021-1027. http://dx.doi.org/10.1016/j.athoracsur.2005.09.020
[6] W. Hilbe, S. Dirnhofer, F. Oberwasserlechner, et al., “Immunohistochemical Typing of Non-Small Cell Lung Cancer on Cryostat Sections: Correlation with Clinical Parameters and Prognosis,” Journal of Clinical Pathology, Vol. 56, No. 10, 2003, pp. 736-741. http://dx.doi.org/10.1136/jcp.56.10.736
[7] D. J. Raz, M. R. Ray, J. Y. Kim, et al., “A Multigene Assay Is Prognostic of Survival in Patients with EarlyStage Lung Adenocarcinoma,” Clinical Cancer Research, Vol. 14, No. 17, 2008, pp. 5565-5570. http://dx.doi.org/10.1158/1078-0432.CCR-08-0544
[8] C. Lu, J.-Ch. Soria, X. Tang, et al., “Prognostic Factors in Resected Stage I Non-Small-Cell Lung Cancer: A Multivariate Analysis of Six Molecular Markers,” Journal of Clinical Oncology, Vol.22, No. 22, 2004, pp. 4575-4583. http://dx.doi.org/10.1200/JCO.2004.01.091
[9] D. H. Harpole, Jr. J. E. Herndon, W. G. Wolfe, et al., “A Prognostic Model of Recurrence and Death in Stage I Non-Small Cell Lung Cancer Utilizing Presentation, Histopathology, and Oncoprotein Expression,” Cancer Research, Vol. 55, No. 1, 1995, pp. 51-56.
[10] S. Holdenrieder, D. Nagel, V. Heinemann, et al., “Predictive and Prognostic Biomarker Models in Advanced Lung Cancer,” Journal of Clinical Oncology, Vol. 26, No. 15S, 2008, p. 19010.
[11] A. Lopez-Encuentra, Lopez-Rios, E. Conde et al., “Composite Anatomical-Clinical-Molecular Prognostic Model in Nonsmall Cell Lung Cancer,” European Respiratory Journal, Vol. 31, No. 7, 2011, pp. 136-142. http://dx.doi.org/10.1183/09031936.00028610
[12] A. Petrie, C. Sabin, “Medical Statistics at a Glance,” In: Petrie A., Sabin C., Eds., Medical Statistics at a Glance, 2nd Edition, Blackwell Publishing, Malden, 2005.
[13] S. Haykin, “Neural networks. A Comprehensive Foundation,” In: Haykin Simon, Ed., Neural Networks. A Comprehensive Foundation, 2nd Edition, Prentice Hall, Upper Saddle River, 1999.
[14] C. Q. Zhu, W. Shih, C. H Ling and M. S. Tsao, “Immunohistochemical Markers of Prognosis in Non-Small Cell Lung Cancer: A Review and Proposal for a Multiphase Approach to Marker Evaluation,” Journal of Clinical Pathology, Vol. 59, No. 8, 2006, pp. 790-800. http://dx.doi.org/10.1136/jcp.2005. 031351
[15] L. R. Leonardus, van der Pijl, O. Birim, et al., “Validation of a Prognostic Model to Predict Survival after NonSmall-Cell Lung Cancer Surgery,” European Journal of Cardio-Thoracic Surgery, Vol. 38, No. 5, 2010, pp. 615-620. http://dx.doi.org/10.1016/j.ejcts.2010.03.028
[16] H. Dosaka-Akita, F. Hommura, T. Mishina, et al., “A Risk-Stratification Model of Non-Small Cell Lung Cancers Using Cyclin E, Ki-67, and rasp21: Different Roles of G1 Cyclins in Cell Proliferation and Prognosis,” Cancer Research, Vol. 61, 2001, pp. 2500-2504.
[17] L. Rubio, F. J. Vera-Sempere, J. A. Lopez-Guerrero, et al., “A Risk Model for Non-Small Cell Lung Cancer Using Clinicopathological Variables, Angiogenesis and Oncoprotein Expression,” Anticancer Research, Vol. 25, No. 1B, 2005, pp. 497-504.

Copyright © 2024 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.