Breast cancer detection by Michaelis-Menten constants via linear programming.

Authors: Blokh D; Afrimzon E; Stambler I; Korech E; Shafran Y; Zurgil N; Deutsch M

Abstract: The Michaelis-Menten constants (K(m) and V(max)) operated by linear programming, were employed for detection of breast cancer. The rate of enzymatic hydrolysis of fluorescein diacetate (FDA) in living peripheral blood mononuclear cells (PBMC), derived from healthy subjects and breast cancer (BC) patients, was assessed by measuring the fluorescence intensity (FI) in individual cells under incubation with either the mitogen phytohemagglutinin (PHA) or with tumor tissue, as compared to control. The suggested model diagnoses three conditions: (1) the subject is diseased, (2) the diagnosis is uncertain, and (3) the subject is not diseased. Out of 50 subjects tested, 44 were diagnosed correctly, in 5 cases the diagnosis was not certain, and 1 subject was diagnosed incorrectly.

Keywords: Breast Neoplasms/*diagnosis; Diagnostic Techniques and Procedures/*statistics & numerical data; Female; Humans; Israel; Programming, Linear/*statistics & numerical data
Journal: Computer methods and programs in biomedicine
Volume: 85
Issue: 3
Pages: 210-3
Date: Dec. 26, 2006
PMID: 17188399
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Categories: Information Theory
Citation:

Blokh D, Afrimzon E, Stambler I, Korech E, Shafran Y, Zurgil N, Deutsch M (2007) Breast cancer detection by Michaelis-Menten constants via linear programming. Computer methods and programs in biomedicine 85: 210-3.



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