Usage of Cox-Regression Model for Forecasting of Survival Rate in Patients with the Early Stage of Non-Small Cell Lung Cancer

Oleksey P. Kolesnik, Anatoliy I. Shevchenko, Yuriy E. Lyakh, Vitaliy G. Gurianov, Pavel A. Alyoshechkin

Department of Medical Biophysics, Medical Informatics and Biostatistics, M. Gorky Donetsk National Medical University, Donetsk, Ukraine.
Department of Oncology, Zaporizhzhya State Medical University, Zaporizhzhya, Ukraine.

DOI: 10.4236/alc.2014.31004                

Abstract

In the past decades a lot of investigations were focused on searching for more accurate markers of lung cancer progression. Researchers indicate that molecular markers may be useful in forecasting of treatment outcome and overall survival rate in patients with non-small cell lung cancer. The aim of our research was to create a forecasting model in order to identify patients with stage I-II 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 December2012 inthe Department of Thoracic Surgery of Zaporizhzhia Regional Clinical Oncologic Dispensary. Surgery was performed for all patients. Adjuvant chemotherapy was performed for 101 patients. In order to carry out multivariate Cox-regression analysis, STATISTICA 6.0 (StatSoft Inc.) program was used. The most significant from 39 variables were selected (tumor size, histological form of tumor, volume of surgical intervention, volume of conducted lymph node dissection, Ki-67 expression, EGFR expression, E-cadherin expression). We propose the computer system which can forecast survival rate in patients with the early stage of non-small cell lung cancer.

Keywords

Forecasting Model; Survival Rate; Non-Small Cell Lung Cancer

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1]	Chen, D.-T., Hsu, Y.-L., William, J.F., et al. (2011) Prognostic and Predictive Value of a Malignancy-Risk Gene Signature in Early-Stage Non-Small Cell Lung Cancer. Journal of the National Cancer Institute, 103, 1859-1870. http://dx.doi.org/10.1093/jnci/djr420
[2]	D’Amico, T.A., Massey, M., Herndon, J.E., et al. (1999) 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, 117, 736-743. http://dx.doi.org/10.1016/S0022-5223(99)70294-1
[3]	Konopa, K. (2010) Do We Have Markers to Select Patients for Adjuvant Therapies of Non-Small-Cell Lung Cancer? Annals of Oncology, 7, vii199-vii202.
[4]	Park, S.Y., Lee, H.-S., Jang, H.-J., et al. (2011) Tumor Necrosis as a Prognostic Factor for Stage IA Non-Small Cell Lung Cancer. The Annals of Thoracic Surgery, 91, 1668-1673. http://dx.doi.org/10.1016/j.athoracsur.2010.12.028
[5]	Zhu, C.-Q., Shih, W., Ling, C.-H. and Tsao, M.-S. (2006) 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, 59, 790-800. http://dx.doi.org/10.1136/jcp.2005.031351
[6]	Raz, D.J., Ray, M.R., Kim, J.Y., et al. (2008) A Multigene Assay Is Prognostic of Survival in Patients with Early-Stage Lung Adenocarcinoma. Clinical Cancer Research, 14, 5565-5570. http://dx.doi.org/10.1158/1078-0432.CCR-08-0544
[7]	Harpole Jr., D.H., Herndon, J.E., Wolfe, W.G., et al. (1995) A Prognostic Model of Recurrence and Death in Stage I Non-Small Cell Lung Cancer Utilizing Presentation, Histopathology, and Oncoprotein Expression. Cancer Research, 55, 51-56.
[8]	Holdenrieder, S., Nagel, D., Heinemann, V., et al. (2008) Predictive and Prognostic Biomarker Models in Advanced Lung Cancer. Journal of Clinical Oncology, 26, 19010.
[9]	López-Encuentra, A., López-Ríos, F., Conde, E., et al. (2011) Composite Anatomical-Clinical-Molecular Prognostic Model in Nonsmall Cell Lung Cancer. European Respiratory Journal, 37, 136-142. http://dx.doi.org/10.1183/09031936.00028610
[10]	Lang, T.A., Math, M.S. and Leonov, V.P. (2011) How to Describe the Statistics in Medicine. An Annotated Guide for Authors, Editors and Reviewers. M:, 480.
[11]	Lu, C., Soria, J.-C., Tang, X., et al. (2004) Prognostic Factors in Resected Stage I Non-Small-Cell Lung Cancer: A Multivariate Analysis of Six Molecular Markers. Journal of Clinical Oncology, 22, 4575-4583. http://dx.doi.org/10.1200/JCO.2004.01.091
[12]	Van der Pijl, L.L.R., Birim, O., Van Gameren, M., et al. (2010) Validation of a Prognostic Model to Predict Survival after Non-Small-Cell Lung Cancer Surgery. European Journal Cardio-Thoracic Surgery, 38, 615-620. http://dx.doi.org/10.1016/j.ejcts.2010.03.028
[13]	Rubio, L., Vera-Sempere, F.J., Lopez-Guerrero, J.A., et al. (2005) A Risk Model for Non-small Cell Lung Cancer Using Clinicopathological Variables, Angiogenesis and Oncoprotein Expression. Anticancer Research, 25, 497-504.
[14]	Dosaka-Akita, H., Hommura, F., Mishina, T., et al. (2001) A Risk-Stratification Model of Non-Small Cell Lung Cancers Using Cyclin E, Ki-67, Andrasp21: Different Roles of G1 Cyclins in Cell Proliferation and Prognosis. Cancer Research, 61, 2500-2504.
[15]	Hilbe, W., Dirnhofer, S., Oberwasserlechner, F., et al. (2003) Immunohistochemical Typing of Non-Small Cell Lung Cancer on Cryostat sections: Correlation with Clinical Parameters and Prognosis. Journal of Clinical Pathology, 56, 736-741. http://dx.doi.org/10.1136/jcp.56.10.736
[16]	Williams, B.A., Sugimura, H., Endo, C., et al. (2006) Predicting Postrecurrence Survival among Completely Resected Nonsmall-Cell Lung Cancer Patients. The Annals of Thoracic Surgery, 81, 1021-1027. http://dx.doi.org/10.1016/j.athoracsur.2005.09.020