Comparative analysis of cell parameter groups for breast cancer detection

David Blokh, Ilia Stambler, Elena Afrimzon, Max Platkov, Yana Shafran, Eden Korech, Judith Sandbank, Naomi Zurgil, Mordechai Deutsch

Research output: Contribution to journalArticlepeer-review

6 Scopus citations


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).

Original languageEnglish
Pages (from-to)239-249
Number of pages11
JournalComputer Methods and Programs in Biomedicine
Issue number3
StatePublished - Jun 2009

Bibliographical note

Funding Information:
This research was supported by The Komen Foundation, The Horowitz Foundation and by US Army Medical Research and Material Command Grant DAMD17-01-1-0131.


  • Breast cancer (BC)
  • Fluorescence polarization (FP)
  • Information Theory
  • Mitogenic stimulation
  • Nonparametric Statistics
  • Peripheral blood mononuclear cells (PBMC)
  • Selecting parameter groups


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