IngentaConnect A Probability-Based Spectroscopic Diagnostic Algorithm for Simult...

Authors: Majumder, Shovan K.¹; Gebhart, Steven¹; Johnson, Mahlon D.²; Thompson, Reid³; Lin, Wei-Chiang⁴; Mahadevan-Jansen, Anita¹

Source: Applied Spectroscopy, Volume 61, Issue 5, Pages 94A-102A and 459-569 (May 2007) , pp. 548-557(10)

Publisher: Society for Applied Spectroscopy

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 in vivo 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.

Keywords: OPTICAL SPECTROSCOPY; AUTOFLUORESCENCE; DIFFUSE REFLECTANCE; MULTI-CLASS CLASSIFICATION; DIAGNOSTIC ALGORITHM; POSTERIOR PROBABILITY; BRAIN TUMOR; TUMOR MARGIN; MAXIMUM REPRESENTATION AND DISCRIMINATION FEATURE; MRDF; SPARSE MULTINOMIAL LOGISTIC REGRESSION; SMLR

Document Type: Research article

DOI: 10.1366/000370207780807704

Affiliations: 1: Dept. of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee 37235 2: School of Medicine and Dentistry, University of Rochester, Rochester, New York 14642 3: Dept. of Neurological Surgery, Vanderbilt University, Nashville, Tennessee 37235 4: Dept. of Biomedical Engineering, Florida International University, Miami, Florida 33199

Your trusted access to this article has expired.

The full text electronic article is available for purchase. You will be able to download the full text electronic article after payment.

$29.00 plus tax

A Probability-Based Spectroscopic Diagnostic Algorithm for Simultaneous Discrimination of Brain Tumor and Tumor Margins from Normal Brain Tissue