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Significance: Polarized hyperspectral microscopes with the capability of full Stokes vector imaging have potential for many biological and medical applications. Aim: The aim of this study is to investigate polarized hyperspectral imaging (PHSI) for improving the visualization of collagen fibers, which is an important biomarker related to tumor development, and improving the differentiation of normal and tumor cells on pathologic slides. Approach: We customized a polarized hyperspectral microscopic imaging system comprising an upright microscope with a motorized stage, two linear polarizers, two liquid crystal variable retarders (LCVRs), and a compact SnapScan hyperspectral camera. The polarizers and LCVRs worked in tandem with the hyperspectral camera to acquire polarized hyperspectral images, which were further used to calculate four Stokes vectors: S0, S1, S2, and S3. Synthetic RGB images of the Stokes vectors were generated for the visualization of cellular components in PHSI images. Regions of interest of collagen, normal cells, and tumor cells in the synthetic RGB images were selected, and spectral signatures of the selected components were extracted for comparison. Specifically, we qualitatively and quantitatively investigated the enhanced visualization and spectral characteristics of dense fibers and sparse fibers in normal stroma tissue, fibers accumulated within tumors, and fibers accumulated around tumors. Results: By employing our customized polarized hyperspectral microscope, we extract the spectral signatures of Stokes vector parameters of collagen as well as of tumor and normal cells. The measurement of Stokes vector parameters increased the image contrast of collagen fibers and cells in the slides. Conclusions: With the spatial and spectral information from the Stokes vector data cubes (S0, S1, S2, and S3), our PHSI microscope system enhances the visualization of tumor cells and tumor microenvironment components, thus being beneficial for pathology and oncology. [ABSTRACT FROM AUTHOR]

The erratum corrects the name of the first author listed in a reference cited. [ABSTRACT FROM AUTHOR]

Significance: Automatic, fast, and accurate identification of cancer on histologic slides has many applications in oncologic pathology. Aim: The purpose of this study is to investigate hyperspectral imaging (HSI) for automatic detection of head and neck cancer nuclei in histologic slides, as well as cancer region identification based on nuclei detection. Approach: A customized hyperspectral microscopic imaging system was developed and used to scan histologic slides from 20 patients with squamous cell carcinoma (SCC). Hyperspectral images and red, green, and blue (RGB) images of the histologic slides with the same field of view were obtained and registered. A principal component analysis-based nuclei segmentation method was developed to extract nuclei patches from the hyperspectral images and the coregistered RGB images. Spectra-based support vector machine and patch-based convolutional neural networks (CNNs) were implemented for nuclei classification. The CNNs were trained with RGB patches (RGB-CNN) and hyperspectral patches (HSI-CNN) of the segmented nuclei and the utility of the extra spectral information provided by HSI was evaluated. Furthermore, cancer region identification was implemented by image-wise classification based on the percentage of cancerous nuclei detected in each image. Results: RGB-CNN, which mainly used the spatial information of nuclei, resulted in a 0.81 validation accuracy and 0.74 testing accuracy. HSI-CNN, which utilized the spatial and spectral features of the nuclei, showed significant improvement in classification performance and achieved 0.89 validation accuracy as well as 0.82 testing accuracy. Furthermore, the image-wise cancer region identification based on nuclei detection could generally improve the cancer detection rate. Conclusions: We demonstrated that the morphological and spectral information contribute to SCC nuclei differentiation and that the spectral information within hyperspectral images could improve classification performance. [ABSTRACT FROM AUTHOR]

For patients undergoing surgical cancer resection of squamous cell carcinoma (SCCa), cancer-free surgical margins are essential for good prognosis. We developed a method to use hyperspectral imaging (HSI), a noncontact optical imaging modality, and convolutional neural networks (CNNs) to perform an optical biopsy of ex-vivo, surgical gross-tissue specimens, collected from 21 patients undergoing surgical cancer resection. Using a cross-validation paradigm with data from different patients, the CNN can distinguish SCCa from normal aerodigestive tract tissues with an area under the receiver operator curve (AUC) of 0.82. Additionally, normal tissue from the upper aerodigestive tract can be subclassified into squamous epithelium, muscle, and gland with an average AUC of 0.94. After separately training on thyroid tissue, the CNN can differentiate between thyroid carcinoma and normal thyroid with an AUC of 0.95, 92% accuracy, 92% sensitivity, and 92% specificity. Moreover, the CNN can discriminate medullary thyroid carcinoma from benign multinodular goiter (MNG) with an AUC of 0.93. Classical-type papillary thyroid carcinoma is differentiated from MNG with an AUC of 0.91. Our preliminary results demonstrate that an HSI-based optical biopsy method using CNNs can provide multicategory diagnostic information for normal and cancerous head-and-neck tissue, and more patient data are needed to fully investigate the potential and reliability of the proposed technique. [ABSTRACT FROM AUTHOR]

A label-free, hyperspectral imaging (HSI) approach has been proposed for tumor margin assessment. HSI data, i.e., hypercube (x,y,λ), consist of a series of high-resolution images of the same field of view that are acquired at different wavelengths. Every pixel on an HSI image has an optical spectrum. In this pilot clinical study, a pipeline of a machine-learning-based quantification method for HSI data was implemented and evaluated in patient specimens. Spectral features from HSI data were used for the classification of cancer and normal tissue. Surgical tissue specimens were collected from 16 human patients who underwent head and neck (H&N) cancer surgery. HSI, autofluorescence images, and fluorescence images with 2-deoxy-2-[(7-nitro-2,1,3-benzoxadiazol-4-yl)amino]-D-glucose (2-NBDG) and proflavine were acquired from each specimen. Digitized histologic slides were examined by an H&N pathologist. The HSI and classification method were able to distinguish between cancer and normal tissue from the oral cavity with an average accuracy of 90% ± 8%, sensitivity of 89% ± 9%, and specificity of 91% ± 6%. For tissue specimens from the thyroid, the method achieved an average accuracy of 94% ± 6%, sensitivity of 94% ± 6%, and specificity of 95% ± 6%. HSI outperformed autofluorescence imaging or fluorescence imaging with vital dye (2-NBDG or proflavine). This study demonstrated the feasibility of label-free, HSI for tumor margin assessment in surgical tissue specimens of H&N cancer patients. Further development of the HSI technology is warranted for its application in image-guided surgery. [ABSTRACT FROM AUTHOR]

Surgical cancer resection requires an accurate and timely diagnosis of the cancer margins in order to achieve successful patient remission. Hyperspectral imaging (HSI) has emerged as a useful, noncontact technique for acquiring spectral and optical properties of tissue. A convolutional neural network (CNN) classifier is developed to classify excised, squamous-cell carcinoma, thyroid cancer and normal head and neck tissue samples using HSI. The CNN classification was validated by the manual annotation of a pathologist specialized in head and neck cancer. The preliminary results of 50 patients indicate the potential of HSI and deep learning for automatic tissue- labeling of surgical specimens of head and neck patients. [ABSTRACT FROM AUTHOR]