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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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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]
Copyright of Journal of Molecular Structure is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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DbLabel: Engineering Source
An: 22797791
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PubType: Academic Journal
PubTypeId: academicJournal
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  Data: Statistical two-dimensional correlation coefficient mapping of simulated tissue phantom data: Boundary determination in tissue classification for cancer diagnosis
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  Data: <searchLink fieldCode="AR" term="%22Skvortsova%2C+Yulia%22">Skvortsova, Yulia</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Wang%2C+Gufeng%22">Wang, Gufeng</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Geng%2C+M%2E+Lei%22">Geng, M. Lei</searchLink><i> Lei-Geng@uiowa.edu</i>
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  Data: <searchLink fieldCode="JN" term="%22Journal+of+Molecular+Structure%22">Journal of Molecular Structure</searchLink>. Nov2006, Vol. 799 Issue 1-3, p239-246. 8p.
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  Data: <searchLink fieldCode="DE" term="%22Cancer+diagnosis%22">Cancer diagnosis</searchLink><br /><searchLink fieldCode="DE" term="%22Decision+support+systems%22">Decision support systems</searchLink><br /><searchLink fieldCode="DE" term="%22Statistical+decision+making%22">Statistical decision making</searchLink><br /><searchLink fieldCode="DE" term="%22Clinical+medicine%22">Clinical medicine</searchLink>
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  Data: 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]
– Name: AbstractSuppliedCopyright
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  Data: <i>Copyright of Journal of Molecular Structure is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1016/j.molstruc.2006.04.005
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      – Code: eng
        Text: English
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        StartPage: 239
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      – SubjectFull: Cancer diagnosis
        Type: general
      – SubjectFull: Decision support systems
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      – SubjectFull: Statistical decision making
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      – TitleFull: Statistical two-dimensional correlation coefficient mapping of simulated tissue phantom data: Boundary determination in tissue classification for cancer diagnosis
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            NameFull: Skvortsova, Yulia
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            NameFull: Wang, Gufeng
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              Text: Nov2006
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