title: Automated algorithm for breast tissue differentiation in optical coherence tomography.
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.
authors: Physical Sciences, Inc., 20 New England Business Center, Andover, Massachusetts 01810, USA. mujat@psicorp.com
authors: Mircea
authors: M
authors: Mujat
authors: R Daniel
authors: RD
authors: Ferguson
authors: Daniel X
authors: DX
authors: Hammer
authors: Christopher
authors: C
authors: Gittins
authors: Nicusor
authors: N
authors: Iftimia
mesh_terms: D000273:Adipose Tissue / Q000033:anatomy & histology; D000465:Algorithms; D044963:Biopsy, Fine-Needle; D001940:Breast / Q000166:cytology* / Q000473:pathology; D001943:Breast Neoplasms / Q000175:diagnosis / Q000473:pathology*; D003658:Decision Making, Computer-Assisted; D005260:Female; D006801:Humans; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted; D041623:Tomography, Optical Coherence / Q000379:methods*