Multiclass discrimination of cervical precancers using Raman spectroscopy

Raman spectroscopy has the potential to differentiate among the various stages leading to high‐grade cervical cancer such as normal, squamous metaplasia, and low‐grade cancer. For Raman spectroscopy to successfully differentiate among the stages, an applicable statistical method must be developed. A...

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Bibliographic Details
Published in:Journal of Raman spectroscopy 2009-02, Vol.40 (2), p.205-211
Main Authors: Kanter, Elizabeth M., Majumder, Shovan, Vargis, Elizabeth, Robichaux-Viehoever, Amy, Kanter, Gary J., Shappell, Heidi, Jones III, Howard W., Mahadevan-Jansen, Anita
Format: Article
Language:English
Subjects:
Algorithms
Cancer
cervix
Discrimination
dysplasia
optical diagnosis
Raman spectra
Raman spectroscopy
Regression
Spectra
Statistical methods
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Summary:Raman spectroscopy has the potential to differentiate among the various stages leading to high‐grade cervical cancer such as normal, squamous metaplasia, and low‐grade cancer. For Raman spectroscopy to successfully differentiate among the stages, an applicable statistical method must be developed. Algorithms like linear discriminant analysis (LDA) are incapable of differentiating among three or more types of tissues. We developed a novel statistical method combining the method of maximum representation and discrimination feature (MRDF) to extract diagnostic information with sparse multinomial logistic regression (SMLR) to classify spectra based on nonlinear features for multiclass analysis of Raman spectra. We found that high‐grade spectra classified correctly 95% of the time; low‐grade data classified correctly 74% of the time, improving sensitivity from 92 to 98% and specificity from 81 to 96% suggesting that MRDF with SMLR is a more appropriate technique for categorizing Raman spectra. SMLR also outputs a posterior probability to evaluate the algorithm's accuracy. This combined method holds promise to diagnose subtle changes leading to cervical cancer. Copyright © 2008 John Wiley & Sons, Ltd. A novel statistical method combining the method of maximum representation and discrimination feature (MRDF) to extract diagnostic information with sparse multinomial logistic regression (SMLR) to classify spectra on the basis of nonlinear features for multiclass analysis of Raman spectra was developed. High‐grade spectra classified correctly 95% of the time; low‐grade data classified correctly 74% of the time, improving sensitivity from 92 to 98% and specificity from 81 to 96%, suggesting that MRDF with SMLR is a more appropriate technique for categorizing Raman spectra.
ISSN:0377-0486
1097-4555
DOI:10.1002/jrs.2108