Automated algorithm for breast tissue differentiation in optical coherence tomography

Physical Sciences, Inc., 20 New England Business Center, Andover, Massachusetts 01810, USA.
Journal of Biomedical Optics (Impact Factor: 2.86). 05/2009; 14(3):034040. DOI: 10.1117/1.3156821
Source: PubMed


An automated algorithm for differentiating breast tissue types based on optical coherence tomography (OCT) data is presented. Eight parameters are derived from the OCT reflectivity profiles and their means and covariance matrices are calculated for each tissue type from a training set (48 samples) selected based on histological examination. A quadratic discrimination score is then used to assess the samples from a validation set. The algorithm results for a set of 89 breast tissue samples were correlated with the histological findings, yielding specificity and sensitivity of 0.88. If further perfected to work in real time and yield even higher sensitivity and specificity, this algorithm would be a valuable tool for biopsy guidance and could significantly increase procedure reliability by reducing both the number of nondiagnostic aspirates and the number of false negatives.

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Available from: Daniel Ferguson, Jul 10, 2014
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    • "Optical coherence tomography (OCT) [5], [6] is a real-time optical imaging technology with significant potential for breast cancer assessment [7]. It has been shown to provide real-time microscopic images up to 2 mm beneath the tissue surface on fresh, excised human breast cancer samples [8]–[10], and computational techniques have been proposed to differentiate tissue types [11], [12]. OCT has also been demonstrated to image the morphological changes associated with lymph node metastasis in excised samples [13], [14] and the associated lymphovascular network [15]. "
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