An information-theoretical model for breast cancer detection

Methods of Information in Medicine

Volumn 47, Issue 4, 2008, Pages 322-327

  Blokh, D ^a   Zurgil, N ^a   Stambler, I ^a   Afrimzon, E ^a   Shafran, Y ^a   Korech, E ^a   Sandbank, J ^b   Deutsch, Mordechai ^a  

^a BAR ILAN UNIVERSITY   (Israel)

^b ASSAF HAROFEH MEDICAL CENTER   (Israel)

Author keywords

Breast cancer (BC); Fluorescence polarization (FP); Formal diognostic model; Normolized mutual information; Weighted Hamming distance

Indexed keywords

ESTROGEN RECEPTOR; PROGESTERONE RECEPTOR;

ADULT; AGED; ARTICLE; BIOINFORMATICS; BREAST CANCER; CANCER DIAGNOSIS; CARCINOMA IN SITU; CLINICAL ARTICLE; CONTROLLED STUDY; DIAGNOSTIC ACCURACY; FLUORESCENCE POLARIZATION; HUMAN; HUMAN CELL; INFORMATION SCIENCE; K NEAREST NEIGHBOR; PAGET NIPPLE DISEASE; PRIORITY JOURNAL; PROTEIN EXPRESSION; QUALITATIVE ANALYSIS; QUANTITATIVE ANALYSIS; THEORETICAL MODEL;

BREAST NEOPLASMS; FEMALE; FLUORESCENCE POLARIZATION; HUMANS; INFORMATION THEORY; MODELS, STATISTICAL;

EID: 50649094903     PISSN: 00261270     EISSN: None     Source Type: Journal    
DOI: 10.3414/ME0440     Document Type: Article

References (34)

1. Gelfand IM, Rosenfeld BI, Shifrin MA. Essays on Collaboration of Mathematicians and Physicians. Moscow: Nauka; 1989 (Russian).
2. Lukas PJF, Abu-Hanna A. Prognostic methods in medicine. Artif Intell Med 1999; 15: 105-119.
3. Verhayen CJDM, Duin RPW, Groen FCA, Joosen JC, Verbeek PW. Progress report on pattern recognition. Rep Prog Phys 1980; 43: 785-831.
4. Wolberg WH, Mangasarian OL. Multisurface method of pattern separation for medical diagnosis applied to breast cytology. Proc Natl Acad Sci USA 1990; 87: 9193-9196.
5. Mangasarian OL, Street WN, Wolberg WH. Breast cancer diagnosis and prognosis via linear programming. Oper Res 1995; 43: 570-577.
6. Blokh D, Afrimzon E, Stambler I, Korech E, Shafran Y, Zurgil N, Deutsch M. Breast cancer detection by Michaelis-Menten constants via linear programming. Comp Meth Prog Biomed 2007; 85: 210-213.
7. Floyd CE, Yun AJ, Sullivan D, Kornguth P. Prediction of breast cancer malignancy using an artificial neural network. Cancer 1994; 74: 2944-2998.
8. Furundzic D, Djordjevic M, Bekic AJ. Neural networks approach to early breast cancer detection. J Syst Architect 1998; 44: 617-633.
9. Setiono R. Generating concise and accurate classification rules for breast cancer diagnosis. Artif Intell Med 2000; 18: 205-219.
10. Shannon CE. A mathematical theory of communication. Bell Syst Tech J 1948; 27: 379-423, 623-656.
11. Khinchin AI. Mathematical Foundations of Information Theory. New York: Dover; 1957.
12. Hamming RW. Coding and Information Theory. Englewood Cliffs NJ: Prentice Hall; 1986.
13. Cover TM, Thomas JA. Elements of Information Theory. New York: Wiley; 1991.
14. Wolberg WH. Inhibition of migration of human autogenous and allogeneic leukocytes by extracts of patients' cancers. Cancer Res 1971; 31: 798-802.
15. Shapiro HM. Practical Flow Cytometry (3rd ed.). New York: Alan R Liss Inc; 1995.
16. Kaplan M, Trebnyikov E, Berke G. Fluorescence depolarization as an early measure of T Lymphocyte stimulation. J Immunol Methods 1997; 201: 15-24.
17. Hayes DF. Tumor markers for breast cancer. Ann Oncol 1993; 4: 807-819.
18. Disis ML, Knutson KL, Schiffman K, Rinn K, McNeel DG. Pre-existent immunity to the HER-2/neo oncogenic protein in patients with HER-2/neu overexpressing breast and ovarian cancer. Breast Cancer Res Treat 2000; 62: 245-252.
19. Nicolis G, Prigogine I. Exploring Complexity. New York: WH Freeman and Company; 1990.
20. Chou WC, Neifeld MA, Xuan R. Information-based optical design for binary-valued imagery. Appl Optics 2000; 39: 1731-1742.
21. Tourassi GD, Frederick ED, Floyd CEJr. Application of the mutual information criterion for feature selection in computer-aided diagnostics. Med Phys 2001; 28: 2394-2402.
22. Zvarova J, Studeny M. Information theoretical approach to constitution and reduction of medical data. Int J Med Inf 1997; 45: 65-74.
23. Rényi A. On measures of dependence. Acta Math Acad Sci Hung 1959; 10: 441-451.
24. Benish WA. Mutual information as an index of diagnostic test performance. Methods Inf Med 2003; 42: 260-264.
25. Fagen RM. Information measures: statistical confidence limits and inference. J Theor Biol 1978; 73: 61-79.
26. Blokh ASh. Graph Schemes and Algorithms. Minsk: Vishaya Shkola; 1987 (Russian).
27. D'hulst R, Rodgers GJ. The Hamming distance in the minority game. Physica A 1999;270:514-525.
28. Bell CB. Mutual information and maximal correlation as measures of dependence. Ann Math Stat 1962; 33: 587-595.
29. Deutsch M, Tirosh R, Kaufman M, Zurgil N, Weinreb A. Fluorescence polarization as a functional parameter in monitoring living cells:theory and practice. J Fluoresc 2002; 12: 25-44.
30. Perrin F. La fluorscence des solutions. Ann Phys (Paris) 1929; 12: 169-275.
31. Scutt D, Lancaster GA, Manning JT. Breast asymmetry and predisposition to breast cancer. Breast Can Res 2006; 8: R14.
32. Wong STC, Liu KY, Zhou X. Cancer classification and prediction using logistic regression with Bayesian gene selection. J Biomed Inf 2004; 37: 249-259.
33. Schwarzer G, Nagata T, Mattern D, Schmelzeisen R, Schumacher M. Comparison of fuzzy inference, logistic regression, and classification trees (CART). Prediction of cervical lymph node metastasis in carcinoma of the tongue. Methods Inf Med 2003; 42: 572-577.
34. Buhl A, Zofel P. SPSS Version 10. Addison-Wesley, 2001.