PDF file, 133K, Diagnostic distribution of tissue population and mean+/-standard deviation for spectrally derived scattering parameters per diagnostic category.
ARTICLE ABSTRACT
Purpose: A new approach to spectroscopic imaging was developed to detect and discriminate microscopic pathologies in resected breast tissues; diagnostic performance of the prototype system was tested in 27 tissues procured during breast conservative surgery.Experimental Design: A custom-built, scanning in situ spectroscopy platform sampled broadband reflectance from a 150-μm-diameter spot over a 1 × 1 cm2 field using a dark field geometry and telecentric lens; the system was designed to balance sensitivity to cellular morphology and imaging the inherent diversity within tissue subtypes. Nearly 300,000 broadband spectra were parameterized using light scattering models and spatially dependent spectral signatures were interpreted using a cooccurrence matrix representation of image texture.Results: Local scattering changes distinguished benign from malignant pathologies with 94% accuracy, 93% sensitivity, 95% specificity, and 93% positive and 95% negative predictive values using a threshold-based classifier. Texture and shape features were important to optimally discriminate benign from malignant tissues, including pixel-to-pixel correlation, contrast and homogeneity, and the shape features of fractal dimension and Euler number. Analysis of the region-based diagnostic performance showed that spectroscopic image features from 1 × 1 mm2 areas were diagnostically discriminant and enabled quantification of within-class tissue heterogeneities.Conclusions: Localized scatter-imaging signatures detected by the scanning spectroscopy platform readily distinguished benign from malignant pathologies in surgical tissues and showed new spectral-spatial signatures of clinical breast pathologies. Clin Cancer Res; 18(22); 6315–25. ©2012 AACR.
