Scanning in Situ Spectroscopy Pplatform for Imaging Surgical Breast Tissue Specimens

A non-contact localized spectroscopic imaging platform has been developed and optimized to scan 1 x 1 cm² square regions of surgically resected breast tissue specimens with ~150-micron resolution. A color corrected, image-space telecentric scanning design maintained a consistent sampling geometry and uniform spot size across the entire imaging field. Theoretical modeling in ZEMAX allowed estimation of the spot size, which is equal at both the center and extreme positions of the field with ~5% variation across the designed waveband, indicating excellent color correction. The spot sizes at the center and an extreme field position were also measured experimentally using the standard knife-edge technique and were found to be within ~8% of the theoretical predictions. Highly localized sampling offered inherent insensitivity to variations in background absorption allowing direct imaging of local scattering parameters, which was validated using a matrix of varying concentrations of Intralipid and blood in phantoms. Four representative, pathologically distinct lumpectomy tissue specimens were imaged, capturing natural variations in tissue scattering response within a given pathology. Variations as high as 60% were observed in the average reflectance and relative scattering power images, which must be taken into account for robust classification performance. Despite this variation, the preliminary data indicates discernible scatter power contrast between the benign vs malignant groups, but reliable discrimination of pathologies within these groups would require investigation into additional contrast mechanisms

Optical assessment of pathology in surgically resected tissues

Multi-spectral spatially modulated light is used to guide localized spectroscopy of surgically resected tissues for cancer involvement. Modulated imaging rapidly quantifies near-infrared optical parameters with sub-millimeter resolution over the entire field for identification of residual disease in resected tissues. Suspicious lesions are further evaluated using a spectroscopy platform designed to image thick tissue samples at a spatial resolution sensitive to the diagnostic gold standard, pathology. MI employs a spatial frequency domain sampling and model-based analysis of the spatial modulation transfer function to interpret a tissue's absorption and scattering parameters at depth. The spectroscopy platform employs a scanning-beam, telecentric dark-field illumination and confocal detection to image fields up to 1cm2 with a broadband source (480:750nm). The sampling spot size (100μm lateral resolution) confines the volume of tissue probed to within a few transport pathlengths so that multiple-scattering effects are minimized and simple empirical models may be used to analyze spectra. Localized spectroscopy of Intralipid and hemoglobin phantoms demonstrate insensitivity of recovered scattering parameters to changes in absorption, but a non-linear dependence of scattering power on Intralipid concentration is observed due to the phase sensitivity of the measurement system. Both systems were validated independently in phantom and murine studies. Ongoing work focuses on assessing the combined utility of these systems to identify cancer involvement in vitro, particularly in the margins of resected breast tumors

Fractal analysis of scatter imaging signatures to distinguish breast pathologies

Fractal analysis combined with a label-free scattering technique is proposed for describing the pathological architecture of tumors. Clinicians and pathologists are conventionally trained to classify abnormal features such as structural irregularities or high indices of mitosis. The potential of fractal analysis lies in the fact of being a morphometric measure of the irregular structures providing a measure of the object’s complexity and self-similarity. As cancer is characterized by disorder and irregularity in tissues, this measure could be related to tumor growth. Fractal analysis has been probed in the understanding of the tumor vasculature network. This work addresses the feasibility of applying fractal analysis to the scattering power map (as a physical modeling) and principal components (as a statistical modeling) provided by a localized reflectance spectroscopic system. Disorder, irregularity and cell size variation in tissue samples is translated into the scattering power and principal components magnitude and its fractal dimension is correlated with the pathologist assessment of the samples. The fractal dimension is computed applying the box-counting technique. Results show that fractal analysis of ex-vivo fresh tissue samples exhibits separated ranges of fractal dimension that could help classifier combining the fractal results with other morphological features. This contrast trend would help in the discrimination of tissues in the intraoperative context and may serve as a useful adjunct to surgeons.This work has been supported by CYCIT projects DA2TOI (FIS2010-19860) and TFS (TEC2010-20224-C02-02), as well as FPU PhD Scholarship (FPU12/04130), all funded by the Spanish Government

Multispectral reflectance enhancement for breast cancer visualization in the operating room

A color enhancement method to optimize the visualization of breast tumors in cancer pathology is proposed. Light scattering measurements are minimally invasive, and allow the estimation of tissue morphology and composition to guide the surgeon in resection surgeries. The usability of scatter and absorption signatures acquired with a microsampling reflectance spectral imaging system was improved employing an empirical approximation to the Mie theory to estimate the scattering power on a per-pixel basis. The proposed methodology generates a new image with blended color and diagnostic purposes coming from the emphasis or highlighting of specific wavelengths or features. These features can be the specific absorbent tissue components (oxygenated and deoxygenated hemoglobin, etc.), additional parameters as scattering power or amplitude or even the combination of both. The goal is to obtain an improved and inherent tissue contrast working only with the local reflectance of tissue. To this aim, it is provided a visual interpretation of what is considered non-malignant (normal epithelia and stroma, benign epithelia and stroma, inflammation), malignant (DCIS, IDC, ILC) and adipose tissue. Consequently, a fast visualization map of the intracavity area can be offered to the surgeon providing relevant diagnostic information. No labeling or extrinsic indicators are required for proposed methodology and therefore the possibility of transferring absorption and scattering features simultaneously into visualization, fusing their effects into a single image, can guide surgeons efficiently.This work has been supported by the Spanish Government through the CYCIT projects DA2TOI (FIS2010-19860) and TEC2013-47264-C2-1-R

Spectral discrimination of breast pathologies in situ using spatial frequency domain imaging

Introduction: Nationally, 25% to 50% of patients undergoing lumpectomy for local management of breast cancer require a secondary excision because of the persistence of residual tumor. Intraoperative assessment of specimen margins by frozen-section analysis is not widely adopted in breast-conserving surgery. Here, a new approach to wide-field optical imaging of breast pathology in situ was tested to determine whether the system could accurately discriminate cancer from benign tissues before routine pathological processing. Methods: Spatial frequency domain imaging (SFDI) was used to quantify near-infrared (NIR) optical parameters at the surface of 47 lumpectomy tissue specimens. Spatial frequency and wavelength-dependent reflectance spectra were parameterized with matched simulations of light transport. Spectral images were co-registered to histopathology in adjacent, stained sections of the tissue, cut in the geometry imaged in situ. A supervised classifier and feature-selection algorithm were implemented to automate discrimination of breast pathologies and to rank the contribution of each parameter to a diagnosis. Results: Spectral parameters distinguished all pathology subtypes with 82% accuracy and benign (fibrocystic disease, fibroadenoma) from malignant (DCIS, invasive cancer, and partially treated invasive cancer after neoadjuvant chemotherapy) pathologies with 88% accuracy, high specificity (93%), and reasonable sensitivity (79%). Although spectral absorption and scattering features were essential components of the discriminant classifier, scattering exhibited lower variance and contributed most to tissue-type separation. The scattering slope was sensitive to stromal and epithelial distributions measured with quantitative immunohistochemistry. Conclusions: SFDI is a new quantitative imaging technique that renders a specific tissue-type diagnosis. Its combination of planar sampling and frequency-dependent depth sensing is clinically pragmatic and appropriate for breast surgical-margin assessment. This study is the first to apply SFDI to pathology discrimination in surgical breast tissues. It represents an important step toward imaging surgical specimens immediately ex vivo to reduce the high rate of secondary excisions associated with breast lumpectomy procedures

Linear classifier and textural analysis of optical scattering images for tumor classification during breast cancer extraction

Texture analysis of light scattering in tissue is proposed to obtain diagnostic information from breast cancer specimens. Light scattering measurements are minimally invasive, and allow the estimation of tissue morphology to guide the surgeon in resection surgeries. The usability of scatter signatures acquired with a micro-sampling reflectance spectral imaging system was improved utilizing an empirical approximation to the Mie theory to estimate the scattering power on a per-pixel basis. Co-occurrence analysis is then applied to the scattering power images to extract the textural features. A statistical analysis of the features demonstrated the suitability of the autocorrelation for the classification of notmalignant (normal epithelia and stroma, benign epithelia and stroma, inflammation), malignant (DCIS, IDC, ILC) and adipose tissue, since it reveals morphological information of tissue. Non-malignant tissue shows higher autocorrelation values while adipose tissue presents a very low autocorrelation on its scatter texture, being malignant the middle ground. Consequently, a fast linear classifier based on the consideration of just one straightforward feature is enough for providing relevant diagnostic information. A leave-one-out validation of the linear classifier on 29 samples with 48 regions of interest showed classification accuracies of 98.74% on adipose tissue, 82.67% on non-malignant tissue and 72.37% on malignant tissue, in comparison with the biopsy H and E gold standard. This demonstrates that autocorrelation analysis of scatter signatures is a very computationally efficient and automated approach to provide pathological information in real-time to guide surgeon during tissue resection.This work has been supported by CYCIT projects DA2TOI (FIS2010-19860) and TFS (TEC2010-20224-C02-02), as well as FPU PhD Scholarship (FPU12/04130) funded by the Spanish Government

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

The feasibility of spatial frequency domain imaging (SFDI) for breast surgical margin assessment was evaluated in tissue-simulating phantoms and in fully intact lumpectomy specimens at the time of surgery. Phantom data was evaluated according to contrast-detail resolution, quantitative accuracy and model-data goodness of fit, where optical parameters were estimated by minimizing the residual sum of squares between the measured modulation amplitude and its solutions, modeled according to diffusion and scaled-Monte Carlo simulations. In contrast-detail phantoms, a 1.25-mm-diameter surface inclusion was detectable for scattering contrast [Formula: see text]; a fraction of this scattering contrast (7%) was detectable for a 10 mm surface inclusion and at least 33% scattering contrast was detected up to 1.5 mm below the phantom surface, a probing depth relevant to breast surgical margin assessment. Recovered hemoglobin concentrations were insensitive to changes in scattering, except for overestimation at visible wavelengths for total hemoglobin concentrations [Formula: see text]. The scattering amplitude increased linearly with scattering concentration, but the scattering slope depended on both the particle size and number density. Goodness of fit was comparable for the diffusion and scaled-Monte Carlo models of transport in spatially modulated, near-infrared reflectance acquired from 47 lumpectomy tissues, but recovered absorption parameters varied more linearly with expected hemoglobin concentration in liquid phantoms for the scaled-Monte Carlo forward model. SFDI could potentially reduce the high secondary excision rate associated with breast conserving surgery; its clinical translation further requires reduced image reconstruction time and smart inking strategies