Structured light imaging for breast-conserving surgery, part II: texture analysis and classification.

Subdiffuse spatial frequency domain imaging (sd-SFDI) data of 42 freshly excised, bread-loafed tumor resections from breast-conserving surgery (BCS) were evaluated using texture analysis and a machine learning framework for tissue classification. Resections contained 56 regions of interest (RoIs) determined by expert histopathological analysis. RoIs were coregistered with sd-SFDI data and sampled into ∼4 × 4 mm2 subimage samples of confirmed and homogeneous histological categories. Sd-SFDI reflectance textures were analyzed using gray-level co-occurrence matrix pixel statistics, image primitives, and power spectral density curve parameters. Texture metrics exhibited statistical significance (p-value < 0.05) between three benign and three malignant tissue subtypes. Pairs of benign and malignant subtypes underwent texture-based, binary classification with correlation-based feature selection. Classification performance was evaluated using fivefold cross-validation and feature grid searching. Classification using subdiffuse, monochromatic reflectance (illumination spatial frequency of fx = 1.37 mm − 1, optical wavelength of λ = 490 nm) achieved accuracies ranging from 0.55 (95% CI: 0.41 to 0.69) to 0.95 (95% CI: 0.90 to 1.00) depending on the benign­malignant diagnosis pair. Texture analysis of sd-SFDI data maintains the spatial context within images, is free of light transport model assumptions, and may provide an alternative, computationally efficient approach for wide field-of-view (cm2) BCS tumor margin assessment relative to pixel-based optical scatter or color properties alone.

Structured light imaging for breast-conserving surgery, part I: optical scatter and color analysis.

Structured light imaging (SLI) with high spatial frequency (HSF) illumination provides a method to amplify native tissue scatter contrast and better differentiate superficial tissues. This was investigated for margin analysis in breast-conserving surgery (BCS) and imaging gross clinical tissues from 70 BCS patients, and the SLI distinguishability was examined for six malignancy subtypes relative to three benign/normal breast tissue subtypes. Optical scattering images recovered were analyzed with five different color space representations of multispectral demodulated reflectance. Excluding rare combinations of invasive lobular carcinoma and fibrocystic disease, SLI was able to classify all subtypes of breast malignancy from surrounding benign tissues (p-value < 0.05) based on scatter and color parameters. For color analysis, HSF illumination of the sample generated more statistically significant discrimination than regular uniform illumination. Pathological information about lesion subtype from a presurgical biopsy can inform the search for malignancy on the surfaces of specimens during BCS, motivating the focus on pairwise classification analysis. This SLI modality is of particular interest for its potential to differentiate tissue classes across a wide field-of-view (∼100 cm2) and for its ability to acquire images of macroscopic tissues rapidly but with microscopic-level sensitivity to structural and morphological tissue constituents.

Light scattering measured with spatial frequency domain imaging can predict stromal versus epithelial proportions in surgically resected breast tissue.

This study aims to determine if light scatter parameters measured with spatial frequency domain imaging (SFDI) can accurately predict stromal, epithelial, and adipose fractions in freshly resected, unstained human breast specimens. An explicit model was developed to predict stromal, epithelial, and adipose fractions as a function of light scattering parameters, which was validated against a quantitative analysis of digitized histology slides for N = 31 specimens using leave-one-out cross-fold validation. Specimen mean stromal, epithelial, and adipose volume fractions predicted from light scattering parameters strongly correlated with those calculated from digitized histology slides (r = 0.90, 0.77, and 0.91, respectively, p-value <1 × 10 - 6). Additionally, the ratio of predicted epithelium to stroma classified malignant specimens with a sensitivity and specificity of 90% and 81%, respectively, and also classified all pixels in malignant lesions with 63% and 79%, at a threshold of 1. All specimens and pixels were classified as malignant, benign, or fat with 84% and 75% accuracy, respectively. These findings demonstrate how light scattering parameters acquired with SFDI can be used to accurately predict and spatially map stromal, epithelial, and adipose proportions in fresh unstained, human breast tissue, and suggest that these estimations could provide diagnostic value.

Calibration and analysis of a multimodal micro-CT and structured light imaging system for the evaluation of excised breast tissue.

A multimodal micro-computed tomography (CT) and multi-spectral structured light imaging (SLI) system is introduced and systematically analyzed to test its feasibility to aid in margin delineation during breast conserving surgery (BCS). Phantom analysis of the micro-CT yielded a signal-to-noise ratio of 34, a contrast of 1.64, and a minimum detectable resolution of 240 µm for a 1.2 min scan. The SLI system, spanning wavelengths 490 nm to 800 nm and spatial frequencies up to 1.37 [Formula: see text], was evaluated with aqueous tissue simulating phantoms having variations in particle size distribution, scatter density, and blood volume fraction. The reduced scattering coefficient, [Formula: see text] and phase function parameter, γ, were accurately recovered over all wavelengths independent of blood volume fractions from 0% to 4%, assuming a flat sample geometry perpendicular to the imaging plane. The resolution of the optical system was tested with a step phantom, from which the modulation transfer function was calculated yielding a maximum resolution of 3.78 cycles per mm. The three dimensional spatial co-registration between the CT and optical imaging space was tested and shown to be accurate within 0.7 mm. A freshly resected breast specimen, with lobular carcinoma, fibrocystic disease, and adipose, was imaged with the system. The micro-CT provided visualization of the tumor mass and its spiculations, and SLI yielded superficial quantification of light scattering parameters for the malignant and benign tissue types. These results appear to be the first demonstration of SLI combined with standard medical tomography for imaging excised tumor specimens. While further investigations are needed to determine and test the spectral, spatial, and CT features required to classify tissue, this study demonstrates the ability of multimodal CT/SLI to quantify, visualize, and spatially navigate breast tumor specimens, which could potentially aid in the assessment of tumor margin status during BCS.

Improving Adequacy of Small Biopsy and Fine-Needle Aspiration Specimens for Molecular Testing by Next-Generation Sequencing in Patients With Lung Cancer: A Quality Improvement Study at Dartmouth-Hitchcock Medical Center.

CONTEXT: - At our medical center, cytopathologists perform rapid on-site evaluation for specimen adequacy of fine-needle aspiration and touch imprint of needle core biopsy lung cancer samples. Two years ago the molecular diagnostics laboratory at our institution changed to next-generation sequencing using the Ion Torrent PGM and the 50-gene AmpliSeq Cancer Hotspot Panel v2 for analyzing mutations in a 50-gene cancer hot spot panel. This was associated with a dramatic fall in adequacy rate (68%). OBJECTIVE: - To improve the adequacy rate to at least 90% for molecular testing using next-generation sequencing for all specimens collected by rapid on-site evaluation by the cytology laboratory. DESIGN: - After baseline data on adequacy rate of cytology specimens with rapid on-site evaluation for molecular testing had been collected, 2 changes were implemented. Change 1 concentrated all the material in one block but did not produce desired results; change 2, in addition, faced the block only once with unstained slides cut up front for molecular testing. Data were collected in an Excel spreadsheet and adequacy rate was assessed. RESULTS: - Following process changes 1 and 2 we reached our goal of at least 90% adequacy rate for molecular testing by next-generation sequencing on samples collected by rapid on-site evaluation including computed tomography-guided needle core biopsies (94%; 17 of 18) and fine-needle aspiration samples (94%; 30 of 32). CONCLUSION: - This study focused on factors that are controllable in a pathology department and on maximizing use of scant tissue. Optimizing the adequacy of the specimen available for molecular tests avoids the need for a second procedure to obtain additional tissue.

Wide-field quantitative imaging of tissue microstructure using sub-diffuse spatial frequency domain imaging.

Localized measurements of scattering in biological tissue provide sensitivity to microstructural morphology but have limited utility to wide-field applications, such as surgical guidance. This study introduces sub-diffusive spatial frequency domain imaging (sd-SFDI), which uses high spatial frequency illumination to achieve wide-field sampling of localized reflectances. Model-based inversion recovers macroscopic variations in the reduced scattering coefficient [Formula: see text] and the phase function backscatter parameter (γ). Measurements in optical phantoms show quantitative imaging of user-tuned phase-function-based contrast with accurate decoupling of parameters that define both the density and the size-scale distribution of scatterers. Measurements of fresh ex vivo breast tissue samples revealed, for the first time, unique clustering of sub-diffusive scattering properties for different tissue types. The results support that sd-SFDI provides maps of microscopic structural biomarkers that cannot be obtained with diffuse wide-field imaging and characterizes spatial variations not resolved by point-based optical sampling.

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.

Scatter spectroscopic imaging distinguishes between breast pathologies in tissues relevant to surgical margin assessment.

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 cm(2) 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 mm(2) 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.

The correlation of in vivo and ex vivo tissue dielectric properties to validate electromagnetic breast imaging: initial clinical experience.

Electromagnetic (EM) breast imaging provides low-cost, safe and potentially a more specific modality for cancer detection than conventional imaging systems. A primary difficulty in validating these EM imaging modalities is that the true dielectric property values of the particular breast being imaged are not readily available on an individual subject basis. Here, we describe our initial experience in seeking to correlate tomographic EM imaging studies with discrete point spectroscopy measurements of the dielectric properties of breast tissue. The protocol we have developed involves measurement of in vivo tissue properties during partial and full mastectomy procedures in the operating room (OR) followed by ex vivo tissue property recordings in the same locations in the excised tissue specimens in the pathology laboratory immediately after resection. We have successfully applied all of the elements of this validation protocol in a series of six women with cancer diagnoses. Conductivity and permittivity gauged from ex vivo samples over the frequency range 100 Hz-8.5 GHz are found to be similar to those reported in the literature. A decrease in both conductivity and permittivity is observed when these properties are gauged from ex vivo samples instead of in vivo. We present these results in addition to a case study demonstrating how discrete point spectroscopy measurements of the tissue can be correlated and used to validate EM imaging studies.