AN ALGORITHM FOR LEUKEMIA IMMUNOPHENOTYPE PATTERN-RECOGNITION

Since leukaemia-specific leucocyte antigen has not been identified to date, the immunological diagnosis of leukaemia is achieved through the application of a wide set of monoclonal antibodies specific for surfa ce markers on leukaemic cells. Thus, the interpretation of leukaemia i mmunophenotype seems to be a mathematically determined comparison of ' what we found' and 'what we know' about it. The objective of this stud y was to establish an algorithm for transformation of empirical rules into mathematical values to achieve proper decisions. Recognition of l eukaemia phenotype was performed by comparison of phenotyping data wit h reference data, followed by scoring of such comparisons. Systematic scoring resulted in the formation of new numerical variables allocated to each state, whereas a most significant variable was described as a complex measure of compatibility. A system of recognized states was d escribed by mathematical variables measuring the confidence of informa tion systems, i.e. maximal, total and relative entropy. The entire alg orithm was derived by matrix algebra and coded in a high-level program language. The list of the states recognized appeared to be especially helpful in differential diagnosis, occasionally pointing to states th at had not been in the scientist's mind at the start of the analysis.