Abstract

This paper reports the development of a probability-based spectroscopic diagnostic algorithm capable of simultaneously discriminating tumor core and tumor margins from normal human brain tissues. The algorithm uses a nonlinear method for feature extraction based on maximum representation and discrimination feature (MRDF) and a Bayesian method for classification based on sparse multinomial logistic regression (SMLR). Both the autofluorescence and the diffuse-reflectance spectra acquired <i>in vivo</i> from patients undergoing craniotomy or temporal lobectomy at the Vanderbilt University Medical Center were used to train and validate the algorithm. The classification accuracy was observed to be approximately 96%, 80%, and 97% for the tumor, tumor margin, and normal brain tissues, respectively, for the training data set and approximately 96%, 94%, and 100%, respectively, for the corresponding tissue types in an independent validation data set. The inherently multi-class nature of the algorithm facilitates a rapid and simultaneous classification of tissue spectra into various tissue categories without the need for a hierarchical multi-step binary classification scheme. Further, the probabilistic nature of the algorithm makes it possible to quantitatively assess the certainty of the classification and recheck the samples that are classified with higher relative uncertainty.

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