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Journal of Theoretical Medicine
Volume 2 (2000), Issue 4, Pages 317-327
http://dx.doi.org/10.1080/10273660008833058

The Prognosis of Survivance in Solid Tumor Patients Based on Optimal Partitions of Immunological Parameters Ranges

1Laboratory of Mathematical Immunobiophysics, Institute of Biochemical Physics of Russian Academy of Sciences Kosygin str., 4, bld. 8, Moscow, 117977, Russia
2Computer Center of Russian Academy of Sciences Vavilova str., 40, Moscow, 117964, Russia
3Russian Cancer Research Center, Kashirskoye sh., 24, Moscow, 115478, Russia

Received 22 November 1998; Accepted 9 August 1999

Copyright © 2000 Hindawi Publishing Corporation. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

New logical and statistical methods are used for the analysis of relationships between survivance and immunological variables. These methods are based on the search of the regularities (syndromes) in the multidimensional space. The syndromes are the elements of partitions of allowable areas of variables. To estimate the statistical validity of found regularities the new technique based on Monte-Carlo computer simulation was used.

We present some results from immunological research to illustrate the methods of logistical regularities search. Two tasks are described. The broad panel of monoclonal antibodies for differentiation lymphocytic antigens were used for lymphocytes subpopulations analysis. The purpose of the first task was the evaluation of significance of immunological parameters for prediction of 1-year metastasis-free survival in non-metastatic osteosarcoma of extremities. The second task was the construction of the predicting alghorithm for prognosis 2-years survival of patients with stomach cancer. The optimal sets of parameters for prediction of survivance was found for both tasks. We found out the high forecasting informativity of HLA-DR+ cells percentage in the 1st task, and the percentage of adhesion cells (CD50+-lymphocytes) in the 2nd task. Multivariate forecasting alghorithms are developed.