Automated algorithm for breast tissue differentiation in optical coherence tomography.

Full Abstract

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.

Author information

Author/s: Mujat, Mircea (M); Ferguson, R Daniel (RD); Hammer, Daniel X (DX); Gittins, Christopher (C); Iftimia, Nicusor (N);

Affiliation: Physical Sciences, Inc., 20 New England Business Center, Andover, Massachusetts 01810, USA. mujat(-atsign-)psicorp.com

Grants: 1R41CA114896-01A1 (Agency:NCI NIH HHS)

Journal and publication information

Publication Type: Journal Article; Research Support, N.I.H., Extramural

Journal: Journal of biomedical optics (J Biomed Opt), published in United States. (Language: eng)

Reference: -2009 May-Jun; vol 14 (issue 3) : pp 034040

Dates: Created 2009/07/01; Completed 2009/09/22;

PMID: 19566332, status: MEDLINE (last retrieval date: 9/22/2009, IMS Date: )

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