Automated algorithm for breast tissue differentiation in optical coherence tomography.
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.
Mujat
Mircea
M
Physical Sciences, Inc., 20 New England Business Center, Andover, Massachusetts 01810, USA. mujat@psicorp.com
Ferguson
R Daniel
RD
Hammer
Daniel X
DX
Gittins
Christopher
C
Iftimia
Nicusor
N
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*