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Significance: Hyperspectral dark-field microscopy (HSDFM) and data cube analysis algorithms demonstrate successful detection and classification of various tissue types, including carcinoma regions in human post-lumpectomy breast tissues excised during breast-conserving surgeries. Aim: We expand the application of HSDFM to the classification of tissue types and tumor subtypes in pre-histopathology human breast lumpectomy samples. Approach: Breast tissues excised during breast-conserving surgeries were imaged by the HSDFM and analyzed. The performance of the HSDFM is evaluated by comparing the backscattering intensity spectra of polystyrene microbead solutions with the Monte Carlo simulation of the experimental data. For classification algorithms, two analysis approaches, a supervised technique based on the spectral angle mapper (SAM) algorithm and an unsupervised technique based on the K-means algorithm are applied to classify various tissue types including carcinoma subtypes. In the supervised technique, the SAM algorithm with manually extracted endmembers guided by H&E annotations is used as reference spectra, allowing for segmentation maps with classified tissue types including carcinoma subtypes. Results: The manually extracted endmembers of known tissue types and their corresponding threshold spectral correlation angles for classification make a good reference library that validates endmembers computed by the unsupervised K-means algorithm. The unsupervised K-means algorithm, with no a priori information, produces abundance maps with dominant endmembers of various tissue types, including carcinoma subtypes of invasive ductal carcinoma and invasive mucinous carcinoma. The two carcinomas' unique endmembers produced by the two methods agree with each other within <2% residual error margin. Conclusions: Our report demonstrates a robust procedure for the validation of an unsupervised algorithm with the essential set of parameters based on the ground truth, histopathological information. We have demonstrated that a trained library of the histopathology-guided endmembers and associated threshold spectral correlation angles computed against well-defined reference data cubes serve such parameters. Two classification algorithms, supervised and unsupervised algorithms, are employed to identify regions with carcinoma subtypes of invasive ductal carcinoma and invasive mucinous carcinoma present in the tissues. The two carcinomas' unique endmembers used by the two methods agree to <2% residual error margin. This library of high quality and collected under an environment with no ambient background may be instrumental to develop or validate more advanced unsupervised data cube analysis algorithms, such as effective neural networks for efficient subtype classification. [ABSTRACT FROM AUTHOR]

Optical imaging technologies have emerged as viable modalities for clinical diagnosis and patient management. A confluence of events has facilitated this progress: (1) the advancement and validation of technologies that [...], * The traditional surgical pathology assessment requires tissue to be removed from the patient, then processed, sectioned, stained, and interpreted by a pathologist using a light microscope. Today, an array of alternate optical imaging technologies allow tissue to be viewed at high resolution, in real time, without the need for processing, fixation, freezing, or staining. Optical imaging can be done in living patients without tissue removal, termed in vivo microscopy, or also in freshly excised tissue, termed ex vivo microscopy. Both in vivo and ex vivo microscopy have tremendous potential for clinical impact in a wide variety of applications. However, in order for these technologies to enter mainstream clinical care, an expert will be required to assess and interpret the imaging data. The optical images generated from these imaging techniques are often similar to the light microscopic images that pathologists already have expertise in interpreting. Other clinical specialists do not have this same expertise in microscopy, therefore, pathologists are a logical choice to step into the developing role of microscopic imaging expert. Here, we review the emerging technologies of in vivo and ex vivo microscopy in terms of the technical aspects and potential clinical applications. We also discuss why pathologists are essential to the successful clinical adoption of such technologies and the educational resources available to help them step into this emerging role. doi: 10.5858/arpa.2018-0298-RA)

Abstract 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.

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 >28%; 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

IntroductionNationally, 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.MethodsSpatial 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.ResultsSpectral 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.ConclusionsSFDI 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.

Background: Immunohistochemistry‐based protein biomarkers can provide useful prognostic information in cutaneous melanoma. The independent prognostic value of Ki‐67 has been studied with variable results. PReferentially expressed Antigen in MElanoma (PRAME) immunohistochemistry is a useful new ancillary tool for distinguishing cutaneous nevi from melanoma; however, its prognostic value has not been well studied. We evaluated PRAME as a prognostic marker in cutaneous melanoma, compared to Ki‐67. Methods: We analyzed the immunohistochemical expression of PRAME and Ki‐67 in 165 melanocytic lesions, including 92 primary melanomas, 19 metastatic melanomas, and 54 melanocytic nevi using tissue microarrays. PRAME immunostaining was scored based on the percentage of positive nuclei: 0 <1%, 1+ 1%–25%, 2+ 26%–50%, 3+ 51%–75%, and 4+ >75%. The percentage of Ki‐67‐positive tumor nuclei was used to calculate the proliferation index. Results: PRAME and Ki‐67 both showed significantly increased expression in melanomas compared to nevi (p < 0.0001 and p < 0.001, respectively). There was no significant difference in PRAME expression in primary versus metastatic melanomas. By contrast, the Ki‐67 proliferation index was higher in metastatic melanoma than in primary melanoma (p = 0.013). Increased Ki‐67 index correlated with ulceration (p < 0.001), increased Breslow depth (p = 0.001), and higher mitotic rate (p < 0.0001), whereas increased PRAME expression correlated with higher mitotic rate (p = 0.047) and Ki‐67 index (p = 0.007). Increased Ki‐67 index correlated with worse disease‐specific survival in patients with primary melanoma (p < 0.001), but PRAME expression did not show prognostic significance in disease‐specific survival (p = 0.63). In a multivariable analysis of patients with primary melanoma, tumor Breslow depth, ulceration, mitotic rate, and Ki‐67 index were each independent predictors of disease‐specific survival (p = 0.006, 0.02, 0.001, and 0.04, respectively); however, PRAME expression was not predictive of disease‐specific survival (p = 0.64). Conclusion: Ki‐67 is an independent prognostic marker; although increased PRAME expression correlates with the Ki‐67 proliferation index and mitotic rate, PRAME is not an independent prognostic marker for cutaneous melanoma. PRAME and Ki‐67 are useful ancillary tools for distinguishing benign from malignant melanocytic lesions. [ABSTRACT FROM AUTHOR]