Biomedical subjects

T Balachander

Publications and source records attributed to T Balachander.

2 recordsLinked to original sources

PubMed1999

Immunophenotypic diagnosis of acute leukemia by using decision tree induction.

We describe a model for decision making for bone marrow immunophenotypic analysis of acute leukemia. In this study, we used decision tree induction as an information processing system for the analysis of flow cytometry immunophenotype results of bone marrow specimens obtained for the diagnosis of acute leukemia. By using decision tree analysis, we queried which antibodies and at what percentage cut-offs led to particular diagnoses. Flow cytometry results of up to 27 monoclonal antibodies from bone marrow specimens of 175 adult and pediatric cases were used: acute lymphoblastic leukemia (n = 80), myeloid leukemia (n = 44), mixed lineage (n = 16), and reactive marrow (n = 35). The percentage of positive cells was used as input data, and the diagnoses were used as output of the information processing system. Results of the decision tree showed an easy, accurate, and intuitive algorithm that can delineate a hierarchy of antibodies relevant to diagnosis. A correct discrimination of acute myeloid and lymphoid leukemia from benign bone marrow can be inferred by using the results of four to eight from a panel of up to 27 antibodies with an accuracy of 95%. Here, we describe a computer-aided model that uses decision tree induction applied to flow cytometry immunophenotype data. If generalizable, this technique may be an alternative approach to modeling complex information like that seen in hematopathology and may complement the immunologist's interpretation, along with cytochemistry and morphology results, in the diagnosis of acute leukemia.

Acute Disease↗

PubMed1996

Neural network analysis of flow cytometry immunophenotype data.

Acute leukemia is one of the leading malignancies in the United States with a mortality rate strongly influenced by the phenotype. This phenotype is based on detection of cell associated antigens normally expressed during leucopoietic differentiation. In this regard, leukemia classified as lymphoid or myeloid by phenotype is also classified as a candidate for the corresponding chemotherapy protocol. Additionally, the subtype of leukemia based on the degree of differentiation and cell maturity influence prognosis, response to treatment, and median survival times. In this paper, we analyze immunophenotype flow cytometry data toward categorization of leukemia into subcategories based on lineage and differentiation antigen expression. Twenty-eight inputs (derived from the mean fluorescence intensity of up to 27 antibodies, and an additional binary input denoting the past diagnosis of leukemia) are used as input to a neural classifier to categorize a total of 170 cases into the lineage and differentiation categories of leukemia. The neural classifier consisted of a feed forward network trained using back propagation. A complexity regulation term (weight decay) was used to improve the generalization performance of the neural classifier. A training error of 0.0% and a generalization error of 10.3% was obtained for categorization based on lineage, while a training error of 0.0% and a generalization error of 10.0% was obtained for categorization based on differentiation. These results indicate that objective classification of multifaceted phenotypes in leukemia can be achieved for analyzing multiparameter data in flow cytometry and further categorization into the prognostic subtypes.

Adult↗