Multiclass discrimination of cervical precancers using Raman spectroscopy

Elizabeth M. Kanter, Shovan Majumder, Elizabeth Vargis, Amy Robichaux-Viehoever, Gary J. Kanter, Heidi Shappell, Howard W. Jones, Anita Mahadevan-Jansena

Research output: Contribution to journalArticlepeer-review

48 Scopus citations


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.

Original languageEnglish
Pages (from-to)205-211
Number of pages7
JournalJournal of Raman Spectroscopy
Issue number2
StatePublished - Feb 2009


  • Cervix
  • Dysplasia
  • Optical diagnosis
  • Raman spectroscopy


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