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Immunologic Predictors of the Risc of Generalization in Non-Small Cell Lung Cancer Patients after Comlete Resections   Oleg Kshivets, M.D., Ph.D. Department of Surgery ,  Siauliai  Public Hospital,  Lithuania The 15th World Congress of World Society of Cardio-Thoracic Surgeons, Vilnius, Lithuania, 2005
Abstract: IMMUNOLOGIC PREDICTORS OF THE RISK OF GENERALIZATION IN NON-SMALL CELL LUNG CANCER PATIENTS AFTER COMPLETE RESECTIONS   Oleg Kshivets    Department of Surgery, Siauliai Public Hospital, Siauliai, 5400, Lithuania   Background :  some non-small cell lung cancer (LC) patients (LCP) after complete resections  are known to be rapidly progressive and fatal requiring adjuvant treatment while others are not. We examined the immunologic factors associated with the low- and high-risk of generalization of LC after surgery. Methods :  We   analyzed data of 108 consecutive LCP radically operated and monitored in 1987-2004 (males – 94, females – 14; pneumonectomy=45, upper/lobectomy=44, lower/lobectomy=11, upper/lower bilobectomy=7, middle lobectomy=1; stage II=34, stage III=74; squamos cell LC=56, adenocarcinoma=46, large cell=6; T1=38, T2=43, T3=23, T4=4; N0=63, N1=20, N2=25; G1=30, G2=34, G3=44). 59 LCP (age=56.7  0.9 years; tumor size: D=4.3  0.3 cm; life span: LS=1903.8  21.0 days) lived more than 5 years without any features of LC progressing. 49 LCP (age=56.6  1.2 years; D=4.6  0.3 cm; LS=542.7  55.2 days) died because of generalization of LC during the first 5 years after radical procedures. Variables selected for 5YS study were input levels of 64 immunity blood parameters, sex, age, TNMG, cell type, D.  Survival curves were estimated by the Kaplan-Meier method. Differences in curves between groups of LCP were evaluated using a log-rank test. Multivariate Cox modeling, multi-factor clustering, discriminant analysis, structural equation modeling, Monte Carlo, bootstrap simulation and neural networks computing were used to determine any significant dependence.   Results :  Cox modeling displayed that 5-year survival of LCP (n=108) after complete resections significantly depended on: N0-2 (P=0.000), T1-4 (P=0.005), lymphocytes (P=0.000), monocytes (P=0.018), CD19 (P=0.000), CD16 (P=0.001), CD4+2H (P=0.000), CD8VV (P=0.000), CD1 (P=0.001), CD8 (P=0.018), CD4 (P=0.001), stick nuclear neutrophils (P=0.000), NST (P=0.000), circular immune complexes (P=0.000).  Neural networks computing, genetic algorithm selection and bootstrap simulation revealed relationships between 5-year survival of LCP and CD8VV (rank=1), N0-2 (2), CD19 (3), CD4+2H (4), natural antibodies (5), LC cell population (6), index thymus function index (7), protein (8), ratio of monocytes to LC cell population  (9), CDw26 (10), heparin tolerance (11), gender (12), LC growth (13), ratio of CDw26 to LC cell population (14), monocytes (15), hemoglobin (16), prothrombin index (17), circular immune complexes (18), G1-3 (19), D (20), weight (21), T1-4 (22), lymphocytes (23), recalcification time (24), eosinophils (25), erythrocytes (26), fibrinogen-B (27), coagulation time (28), IgM (29), ratio of eosinophils to LC cell population (30).  Correct prediction of LCP survival after radical procedures was 88.9% by logistic regression (odds ratio=64.8), 95.4% by discriminant analysis and 100% by neural networks computing (area under ROC curve=1.0; error=0.001).
Factors: 1) Antropometric Factors…………… 4 2) Immune Testing...……………….. 54   3) Blood Analysis…………………… 26 4) Hemostasis Factors……………….. 8 5) Cell Ratio Factors……………….. 18   6) Lung Cancer Characteristics……. 9 7) Biochemic Factors………………... 5 8) Treatment Characteristics……….. 5 9) Survival Data……………………… 4 In All………………………………. 133
Main Problem of Analysis of Alive Supersystems (e.g. Lung Cancer Patient Homeostasis):  Phenomenon of «Combinatorial Explosion» Number of Clinicomorphological Factors:……...….. 133 Number of Possible Combination for Random Search:……………..……………… n!=133!=1.487e+226   Operation Time of IBM Blue Gene/L Supercomputer (135.5TFLOPS) ………………………… 4.7e+218 Years The Age of Our Universe………..... 1.3e+10 Years
Basis: NP     RP     P        n!   n*n*2(e+n)  or  n log n    n          AI     CSA+S+B     SM
Samplings: Lung Cancer Patients Lived More than 5 Years after Radical Procedures………. 59 Lung Cancer Patients Died Because Generalization During First 5 Years After Radical Procedures……………... 49 In All…………………………………...108 5-Year Survival…………………….54.6%
Complete Pulmonary Resections: Pneumonectomy………………….. 45 Upper/Lower Bilobectomy……...… 7 Upper Lobectomy……………….... 44 Lower Lobectomy……………….... 11 Middle Lobectomy…………………. 1 In All…………………………...…108
Staging: T1……38   N0……63   G1……30 T2……43   N1……20   G2……34 T3……23   N2……25   G3……44 T4……..4   Stage II…34  Stage III...74 Squamo u s Cell Carcinoma……… .. ...…56 Adenocarcinoma……………………….46 Large Cell Carcinoma…………………..6
Immune Testing:
Cumulative Proportion Lung Cancer Patients Surviving after Complete Resections (Kaplan-Meier) (n=108)
Results of Multivariate Proportional Hazard Cox Regression Analysis: Chi2=127.643; df=22; n=108; P=0.000000 Factors Wald df P Exp(B) 95%CI for Exp(B) Lower Upper CD8+VV+% 46.050 1 0.000 1.124 1.087 1.162 CD8+VV+abs 32.925 1 0.000 0.004 0.001 0.025 CD1+abs 10.199 1 0.001 0.000 0.000 0.000 CD8+% 5.552 1 0.018 0.966 0.939 0.994 CD8+abs 11.101 1 0.001 11.521 2.736 48.518 B+% 20.465 1 0.000 0.924 0.892 0.956 CD16+abs 11.348 1 0.001 1.2e+5 133.00 1.1e+8 NSTs 18.574 1 0.000 1.094 1.050 1.139 St.Neutrophils abs 28.981 1 0.000 1.6e+4 487.83 5.8e+5 Monocytes abs 4.316 1 0.038 3.6e+3 1.590 8.3e+6 Lymphocytes% 22.636 1 0.000 1.086 1.050 1.124 CIC 21.396 1 0.000 0.963 0.948 0.979
Results of Multivariate Proportional Hazard Cox Regression Analysis: Chi2=127.643; df=22; n=108; P=0.000000 Factors Wald df P Exp(B) 95%CI for Exp(B) Lower Upper CD16+tot 10.521 1 0.001 0.119 0.033 0.431 CD4+2H+tot 30.228 1 0.000 1.745 1.431 2.128 CD1+tot 7.721 1 0.005 76.662 3.592 1.6e+3 Monocytes tot 5.645 1 0.018 0.159 0.035 0.725 T1-4 13.012 3 0.005 T1-4(1) 0.151 1 0.697 1.334 0.312 5.699 T1-4(2) 0.484 1 0.487 0.616 0.158 2.409 T1-4(3) 2.158 1 0.142 0.329 0.075 1.450 N0-2 27.425 2 0.000 N0-2(1) 7.972 1 0.005 0.344 0.164 0.722 CD4+/Cancer Cells 14.547 1 0.000 0.196 0.085 0.453
Results of Discriminant Analysis in Prediction of Lung Cancer Patients Survival after Lobectomies and Pneumonectomies (n=108) Discriminant Function Analysis Summary   Wilks' Lambda: 0.230  approx. F (60,47)=2.619  p< 0.0004 Wilks'  Partial  F-remove    P-level  Lambda  Lambda  (1,421)      N0-2 .288494 .798046 11.89384  .001199 CD8+VV+% .272237 .845700 8.57524 .005240 B+%   .254813 .903530 5.01819 .029850 CIC .249275 .923603 3.88769 .054543 CD4+2H+% .246179 .935218 3.25564 .077588 CD8+VV+tot .245170 .939066 3.04971 .087285 NST2 .242024 .951274 2.40743 .127469 CDw26+abs .240159 .958661 2.02673 .161158 PHN .240297 .958112 2.05478 .158348 Seg.Neutrophils% .239719 .960422 1.93680 .170568 Lymphocytes% .239040 .963149 1.79829 .186365 NA .238421 .965648 1.67198 .202313 CD8+% .237814 .968113 1.54806 .219592 PFT   .238148 .966758 1.61608 .209895 ISF .237157 .970797 1.41385 .240391 CDw26+/CC .236674 .972780 1.31515 .257268 CD4+/CC .237153 .970813 1.41305 .240522 B+/CC .230458 .999018 0.04621 .830731
Results of Logistic Regression Analysis in Prediction of Lung Cancer Patients Survival after Complete Resections (n=108); Chi2=88.714; df=19; P=0.00000; Odds ratio=64.800     Est.  S.E. Wald   P Odds 95.0% .Od.Ratio    Ratio Lower Upper Const.B    5.619  3.193 3.095  .0785 275.578 0.483 1.5e+5 T1-4  -0.321  0.623 0.265 0.6067 0.725 0.210 2.504 CD8+VV+% -0.216  0.077 7.814  0.0052 0.806 0.691 0.940 CD8+VV+abs    8.332 3.987 4.367  0.0367 4152.486 1.504 1.1e+7 CD1+abs 46.682  22.224 4.412  0.0357 CD8+% 0.141  0.056 6.345  0.0118 1.151 1.030 1.287 CD4+abs -0.771  2.419 0.102  0.7499 0.462 0.004 56.624 B+% 0.257  0.086 8.983  0.0027 1.294 1.091 1.534 CD16+abs   -22.709  10.715 4.491  0.0341 0.000 0.000 0.243 NSTs -0.053  0.072 0.529  0.4672 0.949 0.822 1.095 St.Neutroph abs  -12.805  6.249 4.200  0.0404 0.000 0.000 0.679 Monocytes abs  -8.340  14.650 0.320  0.5700 0.000 0.000 1.0e+9 Lymphocytes abs-2.866  1.491  3.694  0.0546 0.057 0.003 1.102 CIC    0.031  0.028 1.264  0.2610 1.032 0.129 14.327 CD4+/CC    0.309  1.184 0.068  0.7942 1.036 0.129 14.327 CD16+tot   4.908  2.106 5.429 0.0198 135.315 2.059 8894.403 CD4+2H+tot  -0.617  0.359 2.944 0.0862 0.540 0.264 1.102 CD1+tot  -9.299  4.555 4.167 0.0412 0.000 0.000 0.782 Monocytes tot   1.733  2.902 0.357 0.5503 5.658 0.018 1807.255 N0-2   -1.425  0.520 7.521 0.0061 0.241 0.086 0.675
Clastering in in Prediction of Lung Cancer Patients Survival after Complete Resections (n=108)
Clastering in in Prediction of Lung Cancer Patients Survival after Complete Resections (n=108)
Neural Networks in Prediction of Lung Cancer Patients Survival after Complete Resections (n=108) Losses   5-year survivors  Baseline Errors=0.001074; Total   49   59 Area under ROC curve=1 .00;  Correct   49   59 Correct Classification Rate= 100% Wrong  0   0 Genetic Algorithm Selection Useful for   Lymphocytes/CC  B/CC  CD16/CC  CD4+2H+/CC  CD8+VV+/CC  CD8/CC Survival   Yes    Yes  Yes  Yes  Yes  Yes
Results of Neural Networks Computing in Prediction of Lung Cancer Patients Survival   after Complete Resections (n=108)     Error=0.001074; Area under ROC Curve=1.0; Correct Classification Rate=100% Factor Rank Error Ratio CD8+VV+% 1 0.183 169.33 N0-2 2 0.129 120.30 B (CD19+)% 3 0.128 118.93 CD4+2H+abs 4 0.122 113.60 NA   5 0.104 97.261 LC Cells 6 0.095 88.299 Thymus FI 7 0.093 86.738 Protein  8 0.084 78.483 Monocytes/CC 9 0.081 75.471 CDw26+tot 10 0.079 73.741 Heparin Tol. 11 0.063 58.667 Gender 12 0.053 49.388 LC Growth 13 0.051 47.182 CDw26+/CC 14 0.036 33.921 Monocytes abs 15 0.036 33.883 Factor Rank Error Ratio Hb 16 0.031 29.625 PI 17 0.031 29.098 CIC 18 0.026 24.520 G1-3 19 0.023 21.495 D 20 0.018 16.520 Weight 21 0.012 11.467 T1-4 22 0.011 10.427 Lymphocytes%23 0.008 7.385 Recalcif.Time 24 0.008 7.348 Eosinophils % 25 0.007 6.312 Erythrocytes 26 0.006 5.510 Fibrinogen-B 27 0.004 4.040 Coagul.Time 28 0.004 3.903 IgM 29 0.004 3.318 Eosinophils/CC30 0.003 2.991
Results of  Kohonen Self-Organizing  Neural Networks Computing in Prediction of Lung Cancer Patients Survival  after Complete  Resections  (n=108)
Results of  Kohonen Self-Organizing  Neural Networks Computing in Prediction of Lung Cancer Patients Survival  after Complete  Resections  (n=108)
Results of  Kohonen Self-Organizing  Neural Networks Computing in Prediction of Lung Cancer Patients Survival  after Complete  Resections  (n=108)
Results of Bootstrap Simulation in Prediction of Lung Cancer Patients Survival   after Complete Resections (n=108)   Number of Samples=3333 Significant Factors Rank Kendall’s Tau-A   P< CD8+VV+ % 1 -0.239 0.000 C D8+VV+ abs 2 -0.239 0.000 CD8+VV+tot 3 -0.219 0.000 B % 4 0.216 0.001 CD8+ % 5 0.199 0.01 CD1+ abs 6 -0.191 0.01 CD1+ % 7 -0.183 0.01 N0-2 8 -0.179 0.01 Erythrocytes 9 0.174 0.01 CD1+ tot   10 -0.172 0.01 CD8+VV+/CC 11 -0.163 0.05 CD1+/CC 12 -0.153 0.05 CD8+ abs 13 0.152 0.05 CD4+ abs 14 -0.148 0.05 CD4+ % 15 -0.146 0.05 Heparin Tolerance 16 -0.145 0.05 CD8+/CC 17 0.144 0.05 CD8+ tot 18 0.143 0.05 CD16+ abs 19 -0.142 0.05 CD4+2H+ % 20 -0.140 0.05 CD4+2H+ abs 21 -0.140 0.05 CD4+2H+ tot 22 -0.135 0.05 Hb 23 0.135 0.05
Ratio of B+, CD8+VV+ and CD16+Cell and Cancer Cell Populations  in  Prediction  of Lung Cancer  Patients Survival after Complete Resections  (n= 108 )
Ratio of B+, CD8+VV+ and CD16+Cell and Cancer Cell Populations  in  Prediction  of Lung Cancer  Patients Survival after Complete Resections  (n= 108 )
Ratio of B+, CD8+VV+ and CD16+Cell and Cancer Cell Populations  in  Prediction  of Lung Cancer  Patients Survival after Complete Resections  (n= 108 )
Prediction of  Lung  Cancer Patients Survival after  Complete Resections (n=108) Classification of Cases by Logistic Regression, n=108 (5-Year Survivors--Losses)  Odds Ratio=68.8 Observed  Pred.Losses  Pred.Survivors  Correct Losses   42  7  85.7% 5-Year Survivors  5  5 4   91.5% Total  96  12  88 .9% Classification of Cases by Discriminant Analysis, n=108 (5-Year Survivors--Losses) Observed  Pred.Losses  Pred.Survivors  Correct Losses   46  3  93.9% 5-Year Survivors  2   57   96.6% Total  103  5  95 .4% Classification of Cases by Neural Networks, n=108 (5-Year Survivors--Losses) Observed  Pred.Losses  Pred.Survivors  Correct Losses   49  0   100% 5-Year Survivors  0  59  100% Total  49  59  100 %
Immunity Networks and 5-Year Survival of Lung Cancer Patients, n=108
Holling-Tenner Models of Alive Supersystem “Lung Cancer-Cytotoxic Cell Population ”
Holling-Tenner Models of Alive Supersystem “Lung Cancer-Cytotoxic Cell Population ”
Lung Cancer Dynamics
 
Conclusions: 5-year survival of lung cancer patients after complete resections significantly depended on:  1) level of T-, B- and K-cell circuit; 2) value of monocyte- and macrophage-circuit;  3) level of humoral  immunity; 4) ratio of malignant cell population quantity to immunity cell subpopulations in integral patient’s organism (immune cell ratio factors); 5) cancer characteristics; 6) blood cell circuit; 7) hemostasis system; 8) biochemic homeostasis; 9) anthropometric data.
Oleg Kshivets, M.D., Ph.D.  Thoracic Surgeon, Department of Surgery Siauliai Cancer Center, Tilzes:42-16, LT5400 Siauliai, Lithuania Tel. (37041)416614; Fax 1(270)9687098 [email_address]   http//:myprofile.cos.com/Kshivets

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Kshivets O. Lung Cancer Surgery

  • 1. Immunologic Predictors of the Risc of Generalization in Non-Small Cell Lung Cancer Patients after Comlete Resections Oleg Kshivets, M.D., Ph.D. Department of Surgery , Siauliai Public Hospital, Lithuania The 15th World Congress of World Society of Cardio-Thoracic Surgeons, Vilnius, Lithuania, 2005
  • 2. Abstract: IMMUNOLOGIC PREDICTORS OF THE RISK OF GENERALIZATION IN NON-SMALL CELL LUNG CANCER PATIENTS AFTER COMPLETE RESECTIONS   Oleg Kshivets   Department of Surgery, Siauliai Public Hospital, Siauliai, 5400, Lithuania   Background : some non-small cell lung cancer (LC) patients (LCP) after complete resections are known to be rapidly progressive and fatal requiring adjuvant treatment while others are not. We examined the immunologic factors associated with the low- and high-risk of generalization of LC after surgery. Methods : We analyzed data of 108 consecutive LCP radically operated and monitored in 1987-2004 (males – 94, females – 14; pneumonectomy=45, upper/lobectomy=44, lower/lobectomy=11, upper/lower bilobectomy=7, middle lobectomy=1; stage II=34, stage III=74; squamos cell LC=56, adenocarcinoma=46, large cell=6; T1=38, T2=43, T3=23, T4=4; N0=63, N1=20, N2=25; G1=30, G2=34, G3=44). 59 LCP (age=56.7  0.9 years; tumor size: D=4.3  0.3 cm; life span: LS=1903.8  21.0 days) lived more than 5 years without any features of LC progressing. 49 LCP (age=56.6  1.2 years; D=4.6  0.3 cm; LS=542.7  55.2 days) died because of generalization of LC during the first 5 years after radical procedures. Variables selected for 5YS study were input levels of 64 immunity blood parameters, sex, age, TNMG, cell type, D. Survival curves were estimated by the Kaplan-Meier method. Differences in curves between groups of LCP were evaluated using a log-rank test. Multivariate Cox modeling, multi-factor clustering, discriminant analysis, structural equation modeling, Monte Carlo, bootstrap simulation and neural networks computing were used to determine any significant dependence. Results : Cox modeling displayed that 5-year survival of LCP (n=108) after complete resections significantly depended on: N0-2 (P=0.000), T1-4 (P=0.005), lymphocytes (P=0.000), monocytes (P=0.018), CD19 (P=0.000), CD16 (P=0.001), CD4+2H (P=0.000), CD8VV (P=0.000), CD1 (P=0.001), CD8 (P=0.018), CD4 (P=0.001), stick nuclear neutrophils (P=0.000), NST (P=0.000), circular immune complexes (P=0.000). Neural networks computing, genetic algorithm selection and bootstrap simulation revealed relationships between 5-year survival of LCP and CD8VV (rank=1), N0-2 (2), CD19 (3), CD4+2H (4), natural antibodies (5), LC cell population (6), index thymus function index (7), protein (8), ratio of monocytes to LC cell population (9), CDw26 (10), heparin tolerance (11), gender (12), LC growth (13), ratio of CDw26 to LC cell population (14), monocytes (15), hemoglobin (16), prothrombin index (17), circular immune complexes (18), G1-3 (19), D (20), weight (21), T1-4 (22), lymphocytes (23), recalcification time (24), eosinophils (25), erythrocytes (26), fibrinogen-B (27), coagulation time (28), IgM (29), ratio of eosinophils to LC cell population (30). Correct prediction of LCP survival after radical procedures was 88.9% by logistic regression (odds ratio=64.8), 95.4% by discriminant analysis and 100% by neural networks computing (area under ROC curve=1.0; error=0.001).
  • 3. Factors: 1) Antropometric Factors…………… 4 2) Immune Testing...……………….. 54 3) Blood Analysis…………………… 26 4) Hemostasis Factors……………….. 8 5) Cell Ratio Factors……………….. 18 6) Lung Cancer Characteristics……. 9 7) Biochemic Factors………………... 5 8) Treatment Characteristics……….. 5 9) Survival Data……………………… 4 In All………………………………. 133
  • 4. Main Problem of Analysis of Alive Supersystems (e.g. Lung Cancer Patient Homeostasis): Phenomenon of «Combinatorial Explosion» Number of Clinicomorphological Factors:……...….. 133 Number of Possible Combination for Random Search:……………..……………… n!=133!=1.487e+226 Operation Time of IBM Blue Gene/L Supercomputer (135.5TFLOPS) ………………………… 4.7e+218 Years The Age of Our Universe………..... 1.3e+10 Years
  • 5. Basis: NP  RP  P    n!  n*n*2(e+n) or n log n  n    AI  CSA+S+B  SM
  • 6. Samplings: Lung Cancer Patients Lived More than 5 Years after Radical Procedures………. 59 Lung Cancer Patients Died Because Generalization During First 5 Years After Radical Procedures……………... 49 In All…………………………………...108 5-Year Survival…………………….54.6%
  • 7. Complete Pulmonary Resections: Pneumonectomy………………….. 45 Upper/Lower Bilobectomy……...… 7 Upper Lobectomy……………….... 44 Lower Lobectomy……………….... 11 Middle Lobectomy…………………. 1 In All…………………………...…108
  • 8. Staging: T1……38 N0……63 G1……30 T2……43 N1……20 G2……34 T3……23 N2……25 G3……44 T4……..4 Stage II…34 Stage III...74 Squamo u s Cell Carcinoma……… .. ...…56 Adenocarcinoma……………………….46 Large Cell Carcinoma…………………..6
  • 10. Cumulative Proportion Lung Cancer Patients Surviving after Complete Resections (Kaplan-Meier) (n=108)
  • 11. Results of Multivariate Proportional Hazard Cox Regression Analysis: Chi2=127.643; df=22; n=108; P=0.000000 Factors Wald df P Exp(B) 95%CI for Exp(B) Lower Upper CD8+VV+% 46.050 1 0.000 1.124 1.087 1.162 CD8+VV+abs 32.925 1 0.000 0.004 0.001 0.025 CD1+abs 10.199 1 0.001 0.000 0.000 0.000 CD8+% 5.552 1 0.018 0.966 0.939 0.994 CD8+abs 11.101 1 0.001 11.521 2.736 48.518 B+% 20.465 1 0.000 0.924 0.892 0.956 CD16+abs 11.348 1 0.001 1.2e+5 133.00 1.1e+8 NSTs 18.574 1 0.000 1.094 1.050 1.139 St.Neutrophils abs 28.981 1 0.000 1.6e+4 487.83 5.8e+5 Monocytes abs 4.316 1 0.038 3.6e+3 1.590 8.3e+6 Lymphocytes% 22.636 1 0.000 1.086 1.050 1.124 CIC 21.396 1 0.000 0.963 0.948 0.979
  • 12. Results of Multivariate Proportional Hazard Cox Regression Analysis: Chi2=127.643; df=22; n=108; P=0.000000 Factors Wald df P Exp(B) 95%CI for Exp(B) Lower Upper CD16+tot 10.521 1 0.001 0.119 0.033 0.431 CD4+2H+tot 30.228 1 0.000 1.745 1.431 2.128 CD1+tot 7.721 1 0.005 76.662 3.592 1.6e+3 Monocytes tot 5.645 1 0.018 0.159 0.035 0.725 T1-4 13.012 3 0.005 T1-4(1) 0.151 1 0.697 1.334 0.312 5.699 T1-4(2) 0.484 1 0.487 0.616 0.158 2.409 T1-4(3) 2.158 1 0.142 0.329 0.075 1.450 N0-2 27.425 2 0.000 N0-2(1) 7.972 1 0.005 0.344 0.164 0.722 CD4+/Cancer Cells 14.547 1 0.000 0.196 0.085 0.453
  • 13. Results of Discriminant Analysis in Prediction of Lung Cancer Patients Survival after Lobectomies and Pneumonectomies (n=108) Discriminant Function Analysis Summary Wilks' Lambda: 0.230 approx. F (60,47)=2.619 p< 0.0004 Wilks' Partial F-remove P-level Lambda Lambda (1,421) N0-2 .288494 .798046 11.89384 .001199 CD8+VV+% .272237 .845700 8.57524 .005240 B+% .254813 .903530 5.01819 .029850 CIC .249275 .923603 3.88769 .054543 CD4+2H+% .246179 .935218 3.25564 .077588 CD8+VV+tot .245170 .939066 3.04971 .087285 NST2 .242024 .951274 2.40743 .127469 CDw26+abs .240159 .958661 2.02673 .161158 PHN .240297 .958112 2.05478 .158348 Seg.Neutrophils% .239719 .960422 1.93680 .170568 Lymphocytes% .239040 .963149 1.79829 .186365 NA .238421 .965648 1.67198 .202313 CD8+% .237814 .968113 1.54806 .219592 PFT .238148 .966758 1.61608 .209895 ISF .237157 .970797 1.41385 .240391 CDw26+/CC .236674 .972780 1.31515 .257268 CD4+/CC .237153 .970813 1.41305 .240522 B+/CC .230458 .999018 0.04621 .830731
  • 14. Results of Logistic Regression Analysis in Prediction of Lung Cancer Patients Survival after Complete Resections (n=108); Chi2=88.714; df=19; P=0.00000; Odds ratio=64.800 Est. S.E. Wald P Odds 95.0% .Od.Ratio Ratio Lower Upper Const.B 5.619 3.193 3.095 .0785 275.578 0.483 1.5e+5 T1-4 -0.321 0.623 0.265 0.6067 0.725 0.210 2.504 CD8+VV+% -0.216 0.077 7.814 0.0052 0.806 0.691 0.940 CD8+VV+abs 8.332 3.987 4.367 0.0367 4152.486 1.504 1.1e+7 CD1+abs 46.682 22.224 4.412 0.0357 CD8+% 0.141 0.056 6.345 0.0118 1.151 1.030 1.287 CD4+abs -0.771 2.419 0.102 0.7499 0.462 0.004 56.624 B+% 0.257 0.086 8.983 0.0027 1.294 1.091 1.534 CD16+abs -22.709 10.715 4.491 0.0341 0.000 0.000 0.243 NSTs -0.053 0.072 0.529 0.4672 0.949 0.822 1.095 St.Neutroph abs -12.805 6.249 4.200 0.0404 0.000 0.000 0.679 Monocytes abs -8.340 14.650 0.320 0.5700 0.000 0.000 1.0e+9 Lymphocytes abs-2.866 1.491 3.694 0.0546 0.057 0.003 1.102 CIC 0.031 0.028 1.264 0.2610 1.032 0.129 14.327 CD4+/CC 0.309 1.184 0.068 0.7942 1.036 0.129 14.327 CD16+tot 4.908 2.106 5.429 0.0198 135.315 2.059 8894.403 CD4+2H+tot -0.617 0.359 2.944 0.0862 0.540 0.264 1.102 CD1+tot -9.299 4.555 4.167 0.0412 0.000 0.000 0.782 Monocytes tot 1.733 2.902 0.357 0.5503 5.658 0.018 1807.255 N0-2 -1.425 0.520 7.521 0.0061 0.241 0.086 0.675
  • 15. Clastering in in Prediction of Lung Cancer Patients Survival after Complete Resections (n=108)
  • 16. Clastering in in Prediction of Lung Cancer Patients Survival after Complete Resections (n=108)
  • 17. Neural Networks in Prediction of Lung Cancer Patients Survival after Complete Resections (n=108) Losses 5-year survivors Baseline Errors=0.001074; Total 49 59 Area under ROC curve=1 .00; Correct 49 59 Correct Classification Rate= 100% Wrong 0 0 Genetic Algorithm Selection Useful for Lymphocytes/CC B/CC CD16/CC CD4+2H+/CC CD8+VV+/CC CD8/CC Survival Yes Yes Yes Yes Yes Yes
  • 18. Results of Neural Networks Computing in Prediction of Lung Cancer Patients Survival after Complete Resections (n=108) Error=0.001074; Area under ROC Curve=1.0; Correct Classification Rate=100% Factor Rank Error Ratio CD8+VV+% 1 0.183 169.33 N0-2 2 0.129 120.30 B (CD19+)% 3 0.128 118.93 CD4+2H+abs 4 0.122 113.60 NA 5 0.104 97.261 LC Cells 6 0.095 88.299 Thymus FI 7 0.093 86.738 Protein 8 0.084 78.483 Monocytes/CC 9 0.081 75.471 CDw26+tot 10 0.079 73.741 Heparin Tol. 11 0.063 58.667 Gender 12 0.053 49.388 LC Growth 13 0.051 47.182 CDw26+/CC 14 0.036 33.921 Monocytes abs 15 0.036 33.883 Factor Rank Error Ratio Hb 16 0.031 29.625 PI 17 0.031 29.098 CIC 18 0.026 24.520 G1-3 19 0.023 21.495 D 20 0.018 16.520 Weight 21 0.012 11.467 T1-4 22 0.011 10.427 Lymphocytes%23 0.008 7.385 Recalcif.Time 24 0.008 7.348 Eosinophils % 25 0.007 6.312 Erythrocytes 26 0.006 5.510 Fibrinogen-B 27 0.004 4.040 Coagul.Time 28 0.004 3.903 IgM 29 0.004 3.318 Eosinophils/CC30 0.003 2.991
  • 19. Results of Kohonen Self-Organizing Neural Networks Computing in Prediction of Lung Cancer Patients Survival after Complete Resections (n=108)
  • 20. Results of Kohonen Self-Organizing Neural Networks Computing in Prediction of Lung Cancer Patients Survival after Complete Resections (n=108)
  • 21. Results of Kohonen Self-Organizing Neural Networks Computing in Prediction of Lung Cancer Patients Survival after Complete Resections (n=108)
  • 22. Results of Bootstrap Simulation in Prediction of Lung Cancer Patients Survival after Complete Resections (n=108) Number of Samples=3333 Significant Factors Rank Kendall’s Tau-A P< CD8+VV+ % 1 -0.239 0.000 C D8+VV+ abs 2 -0.239 0.000 CD8+VV+tot 3 -0.219 0.000 B % 4 0.216 0.001 CD8+ % 5 0.199 0.01 CD1+ abs 6 -0.191 0.01 CD1+ % 7 -0.183 0.01 N0-2 8 -0.179 0.01 Erythrocytes 9 0.174 0.01 CD1+ tot 10 -0.172 0.01 CD8+VV+/CC 11 -0.163 0.05 CD1+/CC 12 -0.153 0.05 CD8+ abs 13 0.152 0.05 CD4+ abs 14 -0.148 0.05 CD4+ % 15 -0.146 0.05 Heparin Tolerance 16 -0.145 0.05 CD8+/CC 17 0.144 0.05 CD8+ tot 18 0.143 0.05 CD16+ abs 19 -0.142 0.05 CD4+2H+ % 20 -0.140 0.05 CD4+2H+ abs 21 -0.140 0.05 CD4+2H+ tot 22 -0.135 0.05 Hb 23 0.135 0.05
  • 23. Ratio of B+, CD8+VV+ and CD16+Cell and Cancer Cell Populations in Prediction of Lung Cancer Patients Survival after Complete Resections (n= 108 )
  • 24. Ratio of B+, CD8+VV+ and CD16+Cell and Cancer Cell Populations in Prediction of Lung Cancer Patients Survival after Complete Resections (n= 108 )
  • 25. Ratio of B+, CD8+VV+ and CD16+Cell and Cancer Cell Populations in Prediction of Lung Cancer Patients Survival after Complete Resections (n= 108 )
  • 26. Prediction of Lung Cancer Patients Survival after Complete Resections (n=108) Classification of Cases by Logistic Regression, n=108 (5-Year Survivors--Losses) Odds Ratio=68.8 Observed Pred.Losses Pred.Survivors Correct Losses 42 7 85.7% 5-Year Survivors 5 5 4 91.5% Total 96 12 88 .9% Classification of Cases by Discriminant Analysis, n=108 (5-Year Survivors--Losses) Observed Pred.Losses Pred.Survivors Correct Losses 46 3 93.9% 5-Year Survivors 2 57 96.6% Total 103 5 95 .4% Classification of Cases by Neural Networks, n=108 (5-Year Survivors--Losses) Observed Pred.Losses Pred.Survivors Correct Losses 49 0 100% 5-Year Survivors 0 59 100% Total 49 59 100 %
  • 27. Immunity Networks and 5-Year Survival of Lung Cancer Patients, n=108
  • 28. Holling-Tenner Models of Alive Supersystem “Lung Cancer-Cytotoxic Cell Population ”
  • 29. Holling-Tenner Models of Alive Supersystem “Lung Cancer-Cytotoxic Cell Population ”
  • 31.  
  • 32. Conclusions: 5-year survival of lung cancer patients after complete resections significantly depended on: 1) level of T-, B- and K-cell circuit; 2) value of monocyte- and macrophage-circuit; 3) level of humoral immunity; 4) ratio of malignant cell population quantity to immunity cell subpopulations in integral patient’s organism (immune cell ratio factors); 5) cancer characteristics; 6) blood cell circuit; 7) hemostasis system; 8) biochemic homeostasis; 9) anthropometric data.
  • 33. Oleg Kshivets, M.D., Ph.D. Thoracic Surgeon, Department of Surgery Siauliai Cancer Center, Tilzes:42-16, LT5400 Siauliai, Lithuania Tel. (37041)416614; Fax 1(270)9687098 [email_address] http//:myprofile.cos.com/Kshivets