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Statistical two-dimensional correlation coefficient mapping of simulated tissue phantom data: Boundary determination in tissue classification for cancer diagnosis

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Bibliographic Details
Title: Statistical two-dimensional correlation coefficient mapping of simulated tissue phantom data: Boundary determination in tissue classification for cancer diagnosis
Authors: Skvortsova, Yulia1, Wang, Gufeng1, Geng, M. Lei Lei-Geng@uiowa.edu
Source: Journal of Molecular Structure. Nov2006, Vol. 799 Issue 1-3, p239-246. 8p.
Subjects: Cancer diagnosis, Decision support systems, Statistical decision making, Clinical medicine
Abstract: Abstract: Statistical correlation coefficient mapping has proven to be a useful technique in tissue classification for cancer diagnosis. The classification is achieved by comparing the correlation coefficients for an unknown to a set of selected tissue samples with known pathological conditions. Currently, the correlation coefficient threshold in the classification is empirically determined. In this paper, boundaries of statistical significance between different tissue pathological conditions are established through Bayesian analysis on the Fisher’s z-transformed Pearson’s correlation coefficients between tissue samples. Moreover, probability values are provided in assigning a tissue sample to a specific tissue clinical condition, which is more appreciable in clinical practices. The methodology is examined with a simulated tissue-phantom data set, yielding satisfactory diagnostic results. [Copyright &y& Elsevier]
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Database: Engineering Source
Description
Abstract:Abstract: Statistical correlation coefficient mapping has proven to be a useful technique in tissue classification for cancer diagnosis. The classification is achieved by comparing the correlation coefficients for an unknown to a set of selected tissue samples with known pathological conditions. Currently, the correlation coefficient threshold in the classification is empirically determined. In this paper, boundaries of statistical significance between different tissue pathological conditions are established through Bayesian analysis on the Fisher’s z-transformed Pearson’s correlation coefficients between tissue samples. Moreover, probability values are provided in assigning a tissue sample to a specific tissue clinical condition, which is more appreciable in clinical practices. The methodology is examined with a simulated tissue-phantom data set, yielding satisfactory diagnostic results. [Copyright &y& Elsevier]
ISSN:00222860
DOI:10.1016/j.molstruc.2006.04.005