The prognosis of survivance in solid tumor patients based on optimal partitions of immunological parameters ranges.

*(English)*Zbl 0959.92012Summary: 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 a 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 anti-bodies for differentiation lymphocytic antigens was 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 algorithm for prognosis 2-years survival of patients with stomach cancer. The optimal sets of parameters for prediction of survivance are found for both tasks. We found out the high forecasting informativity of \(\text{HLA-DR}^+\) cells percentage in the 1st task, and the percentage of adhesion cells \(\text{(CD50}^+\)-lymphocytes) in the 2nd task. Multivariate forecasting algorithms are developed.

We present some results from immunological research to illustrate the methods of logistical regularities search. Two tasks are described. The broad panel of monoclonal anti-bodies for differentiation lymphocytic antigens was 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 algorithm for prognosis 2-years survival of patients with stomach cancer. The optimal sets of parameters for prediction of survivance are found for both tasks. We found out the high forecasting informativity of \(\text{HLA-DR}^+\) cells percentage in the 1st task, and the percentage of adhesion cells \(\text{(CD50}^+\)-lymphocytes) in the 2nd task. Multivariate forecasting algorithms are developed.

##### MSC:

92C50 | Medical applications (general) |