This document summarizes a study examining factors that predict outcomes for patients with non-small cell lung cancer who undergo lobectomies or pneumonectomies. Neural networks, Cox regression, and bootstrap simulation were used to analyze data on 511 patients. The analysis found that nodal status (N0-2), tumor growth characteristics, surgery type, grade (G1-3), histology, radiotherapy, adjuvant therapy, and 16 blood factors best predicted whether patients would be 5-year survivors or experience cancer mortality within 5 years of surgery. Neural networks correctly predicted patient outcomes in 99.8% of cases.

1. NEURAL NETWORKS AND BOOTSTRAP SIMULATION IN PREDICTION OF OUTCOME OF NON-SMALL CELL LUNG CANCER PATIENTS AFTER COMPLETE LOBECTOMIES AND PNEUMONECTOMIES Oleg Kshivets, MD, PhD Surgery Department,Siauliai Cancer Center, Lithuania The Society of Cardiothoracic Surgeons of Great Britain and Ireland Annual Scientific Meeting , London , the UK, March 5-8, 2005.

8. Combined & E xtensive R adical P rocedures with R esection of P ericardium, L eft 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.……………………. 143 Sistematic Mediastinal Lymph Node-N2 Dissection………….. 386

16. Product-Limit (Kaplan-Maier) Analysis Results in Prediction of Lung Cancer Patients Survival after Lobectomies and Pneumonectomies (n=511) Graph of Survival Times vs. Cum. Proportion Surviving

17. Cumulative Proportion Lung Cancer Patients Surviving (Kaplan-Meier) (n=511)

18. Cumulative Proportion Lung Cancer Patients Surviving (Kaplan-Meier) (n=511)

19. Cumulative Proportion Lung Cancer Patients Surviving (Kaplan-Meier) (n=511)

20. Cumulative Proportion Lung Cancer Patients Surviving (Kaplan-Meier) (n=511)

21. Cumulative Proportion Lung Cancer Patients Surviving (Kaplan-Meier) (n=511)

25. Results of Multifactor Analysis in Prediction of Lung Cancer Patients Survival after Lobectomies and Pneumonectomies (n=511)

29. Results of Multifactor Clustering of Clinicomorphologic Factors in Prediction of Lung Cancer Patients Survival after Lobectomies and Pneumonectomies (n=481)

30. Results of Correspondence Analysis in Prediction of Lung Cancer Patients Survival after Lobectomies and Pneumonectomies (n= 481 )

31. Results of Correspondence Analysis in Prediction of Lung Cancer Patients Survival after Lobectomies and Pneumonectomies (n= 481 )

32. Results of Correspondence Analysis in Prediction of Lung Cancer Patients Survival after Lobectomies and Pneumonectomies (n= 481 )

33. Results of Correspondence Analysis in Prediction of Lung Cancer Patients Survival after Lobectomies and Pneumonectomies (n= 481 )

35. Classification Tree in Prediction of Lung Cancer Patients Survival after Lobectomies and Pneumonectomies (n=481)

41. Decision Tree in Prediction of Lung Cancer Patients Survival with N0 (n=274) and with N2 (n=92) after Lobectomies and Pneumonectomies

42. Results of Kohonen Self-Organizing Neural Networks Computing in Prediction of Lung Cancer Patients Survival with N 0 (n=274) and with N2 (n=92) after Lobectomies and Pneumonectomies

43. Cumulative Proportion Lung Cancer Patients Surviving (Kaplan-Meier) with N0 (n=297), with N1-2 (n=214), with N1 (n=116) and with N2 (n=98)

44. Cumulative Proportion Lung Cancer Patients Surviving (Kaplan-Meier) with N0 (n=297), with N1-2 (n=214), with N1 (n=116) and with N2 (n=98)

45. Cumulative Proportion Lung Cancer Patients Surviving (Kaplan-Meier) with N0 (n=297), with N1-2 (n=214), with N1 (n=116) and with N2 (n=98)

46. Cumulative Proportion Lung Cancer Patients Surviving (Kaplan-Meier) with N0 (n=297), with N1-2 (n=214), with N1 (n=116) and with N2 (n=98)

49. Results of Bootstrap simulation in Prediction of Lung Cancer Patients Survival after Lobectomies and Pneumonectomies (n=481)

50. Results of Bootstrap simulation in Prediction of Lung Cancer Patients Survival after Lobectomies and Pneumonectomies (n=481)

51. Results of Bootstrap simulation in Prediction of Lung Cancer Patients Survival after Lobectomies and Pneumonectomies (n=481)

54. Ratio of Erythrocytes, Leucocytes, Eosinophils, Healthy Cells and Cancer Cell Populations & Blood Glucose Level in Prediction of Lung Cancer Patients Survival (n= 481 )

55. Ratio of Erythrocytes, Leucocytes, Eosinophils, Healthy Cells and Cancer Cell Populations & Blood Glucose Level in Prediction of Lung Cancer Patients Survival (n= 481 )

56. Ratio of Erythrocytes, Leucocytes, Eosinophils, Healthy Cells and Cancer Cell Populations & Blood Glucose Level in Prediction of Lung Cancer Patients Survival (n= 481 )

57. Ratio of Erythrocytes, Leucocytes, Eosinophils, Healthy Cells and Cancer Cell Populations & Blood Glucose Level in Prediction of Lung Cancer Patients Survival (n= 481 )

58. Networks between Clinicopathologic, Biochemic, Hemostasis & Hematologic Data and 5-Year Survival of Lung cancer Patients after Lobectomies and Pneumonectomies (n=481)

59. Results of Monte Carlo Simulation in Prediction of Lung Cancer Patients Survival after Lobectomies and Pneumonectomies (n=481)

60. Results of Monte Carlo Simulation in Prediction of Lung Cancer Patients Survival after Lobectomies and Pneumonectomies (n=481)

61. Results of Monte Carlo Simulation in Prediction of Lung Cancer Patients Survival after Lobectomies and Pneumonectomies (n=481)

62. SEPATH Networks in Prediction of Lung Cancer Patients Survival after Lobectomies and Pneumonectomies (n=481)

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

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

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

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

67. Lung Cancer Dynamics