Comparative analysis of cell parameter groups for breast cancer detection.

Abstract	We present a method for the comparative analysis of parameter groups according to their correlation to disease. The theoretical basis of the proposed method is Information Theory and Nonparametric Statistics. Normalized mutual information is used as the measure of correlation between parameters, and statistical conclusions are based on ranking. The fluorescence polarization (FP) parameter is considered as the principal diagnostic characteristic. The FP was measured in fluorescein diacetate (FDA)-stained individual peripheral blood mononuclear cells (PBMC), derived from healthy subjects and breast cancer (BC) patients, under different stimulation conditions: by tumor tissue, the mitogen phytohemagglutinin (PHA) or without the stimulants. The FP parameters were grouped according to their correlation with breast cancer. It was established that the greatest difference between cells of BC patients and healthy subjects is found in the PHA test (parameter P1).
Authors	David Blokh, Ilia Stambler, Elena Afrimzon, Max Platkov, Yana Shafran, Eden Korech, Judith Sandbank, Naomi Zurgil, Mordechai Deutsch
Journal	Computer methods and programs in biomedicine (Comput Methods Programs Biomed) Vol. 94 Issue 3 Pg. 239-49 (Jun 2009) ISSN: 1872-7565 [Electronic] Ireland
PMID	19231022 (Publication Type: Comparative Study, Journal Article, Research Support, Non-U.S. Gov't, Research Support, U.S. Gov't, Non-P.H.S.)
Chemical References	Fluoresceins Phytohemagglutinins diacetylfluorescein
Topics	Adult Aged Aged, 80 and over Breast Neoplasms (diagnosis, pathology) Case-Control Studies Computational Biology (methods) Computer Simulation Early Detection of Cancer Female Fluoresceins (pharmacology) Humans Image Processing, Computer-Assisted (methods) Leukocytes, Mononuclear (cytology, pathology) Microscopy, Fluorescence (methods) Middle Aged Pattern Recognition, Automated Phytohemagglutinins (chemistry) Statistics, Nonparametric

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