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Early Detection and Phase Transitions in System “Malignancy-Human Organism” Oleg Kshivets, M.D., Ph.D. Siauliai Cancer Center, Lithuania AACR Special Conference: The Biology and Genetics of Early Detection & Chemoprevention of Cancer Miami, Florida, The USA, 1999

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Page 1: Kshivets O. Cancer, Synergetics, Computer Sciences and Alive Supersystems

Early Detection and Phase Transitions in System “Malignancy-

Human Organism”

Oleg Kshivets, M.D., Ph.D.Siauliai Cancer Center, Lithuania

AACR Special Conference: The Biology and Genetics of Early Detection & Chemoprevention of Cancer

Miami, Florida, The USA, 1999

Page 2: Kshivets O. Cancer, Synergetics, Computer Sciences and Alive Supersystems

Abstract:• EARLY DETECTION AND PHASE TRANSITIONS IN SYSTEM “MALIGNANCY-HUMAN

ORGANISM” • Oleg Kshivets Siauliai Cancer Center, Siauliai, Lithuania

• Purpose: This research studied homeostasis parameters, typical for two extreme states of phase transition: early malignancy (strategic purpose of early detection) and invasive cancer (C).

• Methods: Basis of this research was the data of 1524 cancer patients (CP) with pathologic stage (T1-4N0-2M0-1G1-4): 655 gastric CP (GCP), 525 lung CP (LCP) and 344 bladder CP (BCP) operated and monitored in clinic 1970-1998. All CP had preoperation examination including peripheral blood count, biochemical tests of venous blood using usual unified methods, clinical, anthropometric, X-ray examination, endoscopy, sonography, electrocardiography and also doctor’s examination, if required, computed tomography and radioisotope scanning. After operations the data of intraoperational investigation, information on character of surgery, complications, morphologic C characteristics (size, TNMG, growth, histology, etc.) was registrated. Representativeness of samplings was reached by means of randomization based on unrepeated random selection. Multiple correspondence analysis (A), clustering, A of variance, confirmatory factor A, structural equation modeling and Monte Carlo simulation were used to determine any significant overall regularities between malignancy and CP organism.

• Results: Using complex system analysis, simulation modeling in terms of synergetics, evoinformatics, statements from theories of Hopf, Landau, Turing and Marchuk it was discovered that system “C-patient’s homeostasis” consecutively passed through three phase transitions: 1) phase transition “norm--oncobackground”; 2) phase transition “oncobackground--early malignancy”; 3) phase transition “early malignancy--invasive C”. If diagnosis of first two phase transitions depended on outcomes of early detection and diagnosis, identification of third phase transition resulted in effectiveness of treatment process and 5-year survival of CP. It was also verified that most important figure of this transition for human was quantity of C cell population in organism and average critical threshold of this population was 4.189e+9 per human organism. Below such value there was a temporal dynamic equilibrium between C and patient’s homeostasis and 5-year survival of radically operated CP tended to be 100%. Excess over this threshold resulted in irreversible consequences when effectiveness of treatment went down up to 10-15%. It was discovered that phase transition of early malignancy into invasive C of any localization significantly depended on: 1) input level of blood cell circuit; 2) ratio of C cell population quantity to blood cell subpopulations in integral CP organism (cell ratio factors); 3) C characteristics (C cell population quantity in CP organism, TNMG); 4) some blood biochemical factors; 5) anthropometric data.

Page 3: Kshivets O. Cancer, Synergetics, Computer Sciences and Alive Supersystems

Samplings:

• Lung Cancer Patients (T1-4N0-2M0-1)…….525• Gastric Cancer Patients (T1-4N0-2M0-1)….655• Bladder Cancer Patients (T1-4N0-2M0-1)…344• In All .………………………...……………..1524

• Patients with Non-Malignant Pathology….3977 • Practically Healthy Old People…………...1464

Page 4: Kshivets O. Cancer, Synergetics, Computer Sciences and Alive Supersystems

Prognostic Role of Cancer DiameterBivariate Histogram: Life Span and Bladder Cancer Diameter (cm)

Life Span (day)Bladder Cancer Diameter (cm)

No of obs

-20000 2000400060008000100001200014000

12345678910111213

20

40

60

80

100

Bivariate Histogram: Life Span of LCP and Cancer Diameter

n=404

Page 5: Kshivets O. Cancer, Synergetics, Computer Sciences and Alive Supersystems

Main Problem of Analysis of Living Supersystems:

Phenomenon of «Combinatorial Explosion»

• Average Number of Routine Blood Parameters:…… 28• Number of Possible Combination • for Random Search:……………….... n!=28!=3.049e+29 • Computer Operation Time of The 7G Teracomputer

(1000TFLOPS) (The 21st Century)… 9.7 Million Years

Page 6: Kshivets O. Cancer, Synergetics, Computer Sciences and Alive Supersystems

Basis

• NP RP P • n! n*n*2(e+n) or n log n n

• AI CSA+S+B SM

Page 7: Kshivets O. Cancer, Synergetics, Computer Sciences and Alive Supersystems

Model «Cancer Cells(Cr)--Human Killer Cells(Kl)»

• Ćr=Cr(1-Kl·μCr/λKl);• Ќl=(Kl·μCr/λKl)·[25·Cr/(4.189+• +2.5·Cr)-Cr-1];

Page 8: Kshivets O. Cancer, Synergetics, Computer Sciences and Alive Supersystems

Regularities between Dynamics Cancer Cell Population and Patient's Survival Rate

Page 9: Kshivets O. Cancer, Synergetics, Computer Sciences and Alive Supersystems

Phase Transitions in System «Homeostasis-Malignancy»

Page 10: Kshivets O. Cancer, Synergetics, Computer Sciences and Alive Supersystems

The Three Phase Transitions in the System «Malignancy-Human’s Organism»

Page 11: Kshivets O. Cancer, Synergetics, Computer Sciences and Alive Supersystems

Phase Transition Early Malignancy into Invasive Cancer

Page 12: Kshivets O. Cancer, Synergetics, Computer Sciences and Alive Supersystems

Role of Cell Ratio Factors in Phaze Transition Erly Cancer into Invasive Cancer

Page 13: Kshivets O. Cancer, Synergetics, Computer Sciences and Alive Supersystems

Regression Models (Lym/CC--Life Span of GCP, n=224; BCP, n=120 and LCP, n=162; 18th Degree Polynomial Fit): y=a+bx+cx2+dx 3+...

Lym/CC--Life Span LCP (n=162); r=0.263;P<0.01

X Axis (Lym/CC)

Y A

xis (

Life

Spa

n)

0.0 0.9 1.8 2.7 3.6 4.5 5.4

Lym/CC--Life Span BCP (n=120); r=0.296;P<0.01

X Axis (Lym/CC)

Y A

xis (

Life

Spa

n)

0.0 1.1 2.3 3.4 4.5 5.6 6.7

Lym/CC--Life Span GCP (n=224); r=0.328;P<0.001

X Axis (Lym/CC)

Y A

xis (

Life

Spa

n)

0.0 1.4 2.8 4.2 5.5 6.9 8.3

Page 14: Kshivets O. Cancer, Synergetics, Computer Sciences and Alive Supersystems

Cell Ratio Factors in Prognosis5-Year Survival Cancer Patients

Normal Probability PlotNormalized Residuals

Lung Cancer Patients (n=404) Model: Cell Ratio Factors-Survival

Value

Exp

ecte

d N

orm

al V

alue

-3

-2

-1

0

1

2

3

-4 -2 0 2 4 6 8 10 12 14

Normal Probability PlotNormalized Residuals

Gastric Cancer Patients (n=376) Model: Cell Ratio Factors-Survival

Value

Exp

ecte

d N

orm

al V

alue

-3

-2

-1

0

1

2

3

-3 -2 -1 0 1 2 3 4 5

Normal Probability Plot

Normalized Residuals

Bludder Cancer Patients (n=344)

Model: Cell Ratio Factors-Survival

Value

Exp

ecte

d N

orm

al V

alue

-3

-2

-1

0

1

2

3

-2 0 2 4 6 8

Page 15: Kshivets O. Cancer, Synergetics, Computer Sciences and Alive Supersystems

Results of Monte Carlo Simulation

-2,334 -1,897 -1,461 -1,024 -0,587 -0,150 0,286 0,723 1,160 1,597 above

From: Monte Carlo Data -- Replication 1 (bl1.sta)

Bludder Cancer Patients (n=344)

Cell Ratio Factors in Prediction of BCP Survival

-0,046 0,209 0,463 0,718 0,973 1,227 1,482 1,736 1,991 2,245 above

From: Monte Carlo Data -- Replication 1 (lc1.sta)Lung Cancer Patients (n=404)

Cell Ratio Factors in Prediction LCP Survival

-0,731 -0,593 -0,455 -0,317 -0,179 -0,041 0,097 0,235 0,372 0,510 above

From: Monte Carlo Data -- Replication 1 (gc11.sta)Gastric Cancer Patients (n=376)

Cell Ratio Factors in Prediction GCP Survival

Page 16: Kshivets O. Cancer, Synergetics, Computer Sciences and Alive Supersystems

Results of Structural Equotion Modeling

Normal Probability Plot (Conf.Modeling)Normalized Residuals

Lung Cancer Patients (n=404)

Model: Survival-Blood Cell Circuit-Cancer-Biochem.Homeostasis

Value

Exp

ecte

d N

orm

al V

alue

-3,5

-2,5

-1,5

-0,5

0,5

1,5

2,5

3,5

-16 -12 -8 -4 0 4 8 12 16

Bludder Cancer n=344Struct.Eq.Modeling

Normal Probability PlotNormalized Residuals

Bludder Cancer Patients (n=344)Model: Survival-Blood Cell Circuit-Cancer-Biochem.Homeostasis

Value

Exp

ecte

d N

orm

al V

alue

-3

-2

-1

0

1

2

3

-8 -4 0 4 8 12

Normal Probability PlotNormalized Residuals

Gastric Cancer Patients (n=376)

Model: Survival-Blood Cell Circuit-Cancer-Biochem.Homeostasis

Value

Exp

ecte

d N

orm

al V

alue

-3,5

-2,5

-1,5

-0,5

0,5

1,5

2,5

3,5

-16 -10 -4 2 8 14 20

Page 17: Kshivets O. Cancer, Synergetics, Computer Sciences and Alive Supersystems

Early and Differential Diagnosis of Malignancies

Page 18: Kshivets O. Cancer, Synergetics, Computer Sciences and Alive Supersystems

The Results of Multiple Correspondence and Claster Analysis of Blood Indexes in

Oncoscreening

Page 19: Kshivets O. Cancer, Synergetics, Computer Sciences and Alive Supersystems

Interdependencies between Blood Parameters, Indexes and Cancer Cell

Population

Page 20: Kshivets O. Cancer, Synergetics, Computer Sciences and Alive Supersystems

Interdependencies between Blood Indexes and Immune System of Cancer Patients

Page 21: Kshivets O. Cancer, Synergetics, Computer Sciences and Alive Supersystems

Networks Between Phase Transition “Early Cancer-Invasive Cancer”, Life Span and

Homeostasis Data

Page 22: Kshivets O. Cancer, Synergetics, Computer Sciences and Alive Supersystems

Superoncoscreeng-1.0SUPERONCOSCREENING-1.0

SCREENING ANTHROPOMETRY ANAMNESIS LOCALIZATION

SCR-1 SCR-2

SCR-3

ANT-1 ANT-2

ANT-3

LOC1 LOC12LOC2 LOC13LOC3 LOC14LOC4 LOC15LOC5 LOC16LOC6 LOC17LOC7 LOC18LOV8 LOC19LOC9 LOC20LOC10 LOC21 LOC11

MALIGNANT NEOPLASM STATISTICS

PRECANCER

NORM

LOC22

MALE FEMALE

Page 23: Kshivets O. Cancer, Synergetics, Computer Sciences and Alive Supersystems

Superoncodiagnos-1.0SUPERONCODIAGNOSIS-1.0

DIAGNOSIS-1 DIAGNOSIS-2 PRECANCER

NORM

MALIGNANT NEOPLASM LOCALIZATION

POPULATION PHASE TRANSITION STATISTICS

EARLY CANCER INVASIVE CANCER

Page 24: Kshivets O. Cancer, Synergetics, Computer Sciences and Alive Supersystems

Superoncodiagnosis of Metastasazing-1.0

SUPERONCODIAGNOSIS OF METASTASING-1.0

MTS-N MTS-M

MET-1 MET-2 DEP-1 DEP-2

LOCALIZATION MTS

LIVER LUNGCANCEROMATOSISBONES BRAINKIDNEY ADRENALSOVARY SKIN

STAGING STATISTICS MTS

POPULATION MC

PHASE TRANSITION

PT1 PT2 PT3

C1 C2GENERALIZATION

ST1 ST2 ST3EARLY MALIGNANCY

INVASIVE MALIGNANCY

MALIGNANCY OF THE II-III STAGES MALIGNANCY OF THE IV STAGE

Page 25: Kshivets O. Cancer, Synergetics, Computer Sciences and Alive Supersystems

Superoncoimmunology-1.0SUPERONCOIMMUNOLOGY-1.0

IMMUNODIAGNOSIS-1 IMMUNODIAGNOSIS-2

IMMUNODEFICIENCY NORMMALIGNANT NEOPLASM

POPULATION PHASE TRANSITION IMMUNOSTAGING

N M G

EARLY CANCER INVASIVE CANCER

Page 26: Kshivets O. Cancer, Synergetics, Computer Sciences and Alive Supersystems

Superoncoprognosis-1.0

SUPERONCOPROGNOSIS-1.0

PROGNOSIS SURVIVAL-2

PROG-1 PROG-2 PROG-3 E

SURVIVAL LESS 5 YEARS SURVIVAL MORE 5 YEARS

SURVIVAL-1

A B

C

Page 27: Kshivets O. Cancer, Synergetics, Computer Sciences and Alive Supersystems

Total Monitoring System

SOS-1.0HEALTHY PEOPLE

PRECANCER

PERSONS FOR SUSPICION OFMALIGNANCY

SOD-1.0

SOI-1.0NONMALIGNANT

PATHOLOGY

MALIGNANCY

SODM-1.0 EARLY CANCER

INVASIVE CANCER SOP-1.0

II-III STAGES IV STAGE

POPULATION OF THE COUNTRY

Page 28: Kshivets O. Cancer, Synergetics, Computer Sciences and Alive Supersystems

Conclusions:• 1. System “Cancer-patient’s homeostasis” consecutively passed

through three phase transitions: “norm-oncobackground”; “oncobackground-early malignancy”; “early malignancy-invasive cancer”.

• 2. Most important figure of this transition for human was quantity of cancer cell population in organism and average critical threshold of this population was 4.189e+9 per human organism.

• 3. Phase transition of early malignancy into invasive cancer of any localization significantly depended on: input level of blood cell circuit; ratio of cancer cell population quantity to blood cell subpopulations in integral cancer patient’s organism (cell ratio factors); malygnancy characteristics (cancer cell population quantity in organism, TNMG); some blood biochemical factors and anthropometric data.

Page 29: Kshivets O. Cancer, Synergetics, Computer Sciences and Alive Supersystems

Patents:1. Kshivets O.M. Method of Screening and Differential Diagnosis of Malignant

Neoplasms// Patent from 27.04.92.-N2045072.-28pp.. 2. Kshivets O.M. Method of Prognosis of Survival Rate of Radically Operated

Oncopatients// Patent from 10.02.94.-N2101704.-24pp. .3. Kshivets O.M. Method of Prognosis of Survival Rate of Non-Radically Operated

Oncopatients// Patent from 14.03.94.-N2104536.-10pp. .4. Kshivets O.M. Method of Early and Differential Immunodiagnosis of Malignancies//

Patent from 24.10.95.-N2107290.-12pp. .5. Kshivets O.M. Method of Immunodiagnosis of Distant Metastases of Oncopatients//

Patent from 06.10.95.-N2107295.-8pp. .6. Kshivets O.M. Method of Immunodiagnosis of Generalization of Oncopatients// Patent

from 09.10.95.-N2107294.-12pp. .7. Kshivets O.M. Method of Immunodiagnosis of Early and Invasive Oncopathology for

Patients// Patent from 04.05.95.-N2107293.-14pp. .8. Kshivets O.M. Method of Differential Diagnosis of Oncopathology and Pre-Cancer or

Non-Malignant Pathology// Patent from 08.11.94.-N2114431.-18pp.

Page 30: Kshivets O. Cancer, Synergetics, Computer Sciences and Alive Supersystems

Patents:. 9.Kshivets O.M. Method of Measuring the Size of Malignant Neoplasms and Total Quantity of

Malignant Cells in Oncopatient’s Organism Based on the Homeostasis Parameters// Application for Patent from 04.05.95.-N95107201/012623.-8pp. (Positive Decision).

.10. Kshivets O.M. Method of Diagnosis of Distant Metastases of Oncopatients// Application for Patent from 03.11.95.-N95118236/032006.-12pp. (Positive Decision).

.11. Kshivets O.M. Method of Diagnosis of Generalization of Oncopatients// Application for Patent from 20.10.95.-N95117904/031312.-12pp. (Positive Decision).

.12. Kshivets O.M. Method of Diagnosis of Early and Invasive Oncopathology for a Single Patient// Application for Patent from 06.10.95.-N95117338/029690.-13pp. (Positive Decision).

.13. Kshivets O.M. Method of Diagnosis of Malignant Neoplasms Metastasizing in Regional Lymphatic Nodules of a Concrete Patient// Application for Patent from 29.09.95.-N95116510/028981.-10pp. (Positive Decision).

.14. Kshivets O.M. Method of Measuring the Size of the Malignancy and Total Quantity of Malignant Cells in the Oncopatient’s Organism Based on the Immunogram// Application for Patent from 04.05.95.-N95107200/012622.-9pp. (Positive Decision).

.15. Kshivets O.M. Method of Immunodiagnosis of Regional Metastases of Oncopatients// Application for Patent from 06.10.95.-N95117049/029707.-9pp. (Positive Decision).

Page 31: Kshivets O. Cancer, Synergetics, Computer Sciences and Alive Supersystems

Address:• Oleg Kshivets, M.D.,

Ph.D.• Thoracic Surgeon• Department of Surgery• Siauliai Cancer Center• Tilzes:42-16, Siauliai, LT78206, Lithuania• Tel. (37041)416614• [email protected][email protected]• http//:myprofile.cos.com/Kshivets