Scatter Spectroscopic Imaging Distinguishes between Breast Pathologies in Tissues Relevant to Surgical Margin Assessment

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 150μm diameter spot over a 1×1cm2 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 co-occurrence matrix representation of image texture. Results Local scattering changes distinguished benign from malignant pathologies with 94% accuracy, 93% sensitivity, 95% specificity, and 93% positive & 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 1×1mm2 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 demonstrated new spectral-spatial signatures of clinical breast pathologies.

System analysis of spatial frequency domain imaging for quantitative mapping of surgically resected breast tissues

“…Contrast-detail assessment in tissue-simulating phantoms suggests that superficial clusters of DCIS, and not epithelial hyperproliferation within a single lumen, are detectable by SFDI. 52,53 However, the increased depths probed by SFDI evaluate wider margins appropriate for DCIS.…”

Directional Kernel Density Estimation for Classification of Breast Tissue Spectra

“…Here, Laughney et al"s tissue database [1,2] was employed to test the overall classification performance of the proposed algorithm. It is composed of 29 imaged samples of about mm of volume.…”

“…The Spectral Normal Variate serves this purpose well in diffuse reflectance spectroscopy [7,8]. Given a spectrum , its SNV can be easily found with the expression (2) where and are the sample average reflectance and the sample standard deviation of the reflectance vector elements, respectively. This transformation allowed the expression of every spectrum as reflectance variations of a pixel with respect to its average reflectance, in standard deviation units.…”

Noninvasive assessment of hemodynamic and brain metabolism parameters following closed head injury in a mouse model by comparative diffuse optical reflectance approaches

“…Specifically, the value and sign of sp is frequently used to differentiate tissue pathologies. 48,49 Once the absorption coefficient spectra is known [μ a ðλÞ] and combined with the wavelength-dependent molar extinction coefficients ε i ðλÞ, metabolic properties (C i ) like perfusion, oxygenation, and chemical content (hemoglobin concentration, lipid, water, and so on) can be derived by applying the least-squares solution to the BeerLambert law, μ a ðλÞ ¼ P i ε i ðλÞC i , as is commonly employed in tissue optics. 50,51 As mentioned above, imaging is commenced before induction CHI to establish baseline chromophore concentrations and continued during and following experimental intervention.…”