SIGNIFICANT IMPACT OF PHASE TRANSITIONS AND CELL RATIO FACTORS FOR 5-YEAR SURVIVAL OF NON SMALLCELL LUNG CANCER PATIENTS AFTER SURGERY

1. SIGNIFICANT IMPACT OF PHASE TRANSITIONS AND CELL RATIO FACTORS FOR 5-YEAR SURVIVAL OF NON SMALL CELL LUNG CANCER PATIENTS AFTER SURGERY Oleg Kshivets, MD, PhD Surgery Department, Siauliai Public Hospital, Lithuania

5. Combined & E xtensive R adical P rocedures with R esection of P ericardium, A trium, A orta, V ena C ava S uperior, V ena A zygos, C arina, Trachea, D iaphragm, C hest W all , Ribs, etc.……………………. 130 Sistematic Mediastinal Lymph Node-N2 Dissection………….. 490

8. Product-Limit (Kaplan-Maier) Analysis Results in Prediction of Lung Cancer Patients Survival after Lobectomies and Pneumonectomies (n= 490 ) :

9. Survival of Early LCP was Significantly Better Compared to Invasive LCP:

10. Survival of LCP with N0 was Significantly Better Compared to LCP with N1-2:

11. Results of Multivariate Proportional Hazard Cox Regression Modeling in Prediction of LCP Survival after Lobectomies and Pneumonectomies (n=490) : Global  2=131.51; Df=7; P=0.000

12. Results of Discriminant Function Analysis in Prediction of LCP survival after lobectomies and pneumonectomies (n=490) : Wilks' Lambda=0.785; approx. F (4,485)=33.159 P=0.000 Correct Classification Rate=72% Wilks' - Lambda Partial - Lambda F-remove - (1,485) P Toler. 1-Toler. - (R-Sqr.) Phase Transition “Early LC---Invasive LC” 0.800861 0.980512 9.63957 0.002 0.905212 0.094787 Eosinophils/Cancer Cells 0.797277 0.984920 7.42555 0.007 0.837585 0.162415 Lymphocytes/Cancer Cells 0.792552 0.990791 4.50772 0.034 0.822803 0.177197 Phase Transition “N0---N12” 0.879453 0.892889 58.18034 0.000 0.947110 0.052890

13. Results of Multifactor Clustering of Cell RatioFactors in Prediction of Lung Cancer Patients Survival after Lobectomies and Pneumonectomies (n=490): Correct Classification Rate=81%

15. Results of Neural Networks Computing in Prediction of 5-Year Survival of LCP after Lobectomies and Pneumonectomies (n=490):

16. Results of Bootstrap simulation in Prediction of Lung Cancer Patients Survival after Lobectomies and Pneumonectomies (n=4 90 ) :

17. Results of Kohonen Self-Organizing Neural Networks Computing in Prediction of Lung Cancer Patients Survival after Lobectomies and Pneumonectomies (n=490):

18. Holling-Tenner Models of Alive Supersystem “Lung Cancer-Cytotoxic Cell Population ”

19. Lung Cancer Dynamics:

20. SEPATH Networks in Prediction of Lung Cancer Patients Survival after Lobectomies and Pneumonectomies (n=490):