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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]

T lymphocytes expressing CD57 and lacking costimulatory receptors CD27/CD28 have been reported to accumulate with aging, chronic infection, and cancer. These cells are described as senescent, with inability to proliferate but enhanced cytolytic and cytokine-producing capacity. However, robust functional studies on these cells taken directly from cancer patients are lacking. We isolated these T cells and their CD27/28+ counterparts from blood and tumor samples of 50 patients with previously untreated head and neck cancer. Functional studies confirmed that these cells have enhanced ability to degranulate and produce IFN-γ. They also retain the ability to proliferate, thus are not senescent. These data suggest that CD27/28-CD57+ CD8+ T cells are a subset of highly differentiated, CD45RA+ effector memory (T EMRA ) cells with retained proliferative capacity. Patients with > 34% of these cells among CD8+ T cells in the blood had a higher rate of locoregional disease relapse, suggesting these cells may have prognostic significance., Competing Interests: NCS discloses consulting fees from Sensorion, Regeneron, and GeoVax in addition to research funding from Astex Pharmaceuticals. The authors have no conflicting financial interests related to this work., (© 2024 The Author(s). Published with license by Taylor & Francis Group, LLC.)

Head and neck squamous cell carcinoma (HNSCC) has a high mortality rate. In this study, we developed a Stokes-vector-derived polarized hyperspectral imaging (PHSI) system for H&E-stained pathological slides with HNSCC and built a dataset to develop a deep learning classification method based on convolutional neural networks (CNN). We use our polarized hyperspectral microscope to collect the four Stokes parameter hypercubes (S0, S1, S2, and S3) from 56 patients and synthesize pseudo-RGB images using a transformation function that approximates the human eye's spectral response to visual stimuli. Each image is divided into patches. Data augmentation is applied using rotations and flipping. We create a four-branch model architecture where each branch is trained on one Stokes parameter individually, then we freeze the branches and fine-tune the top layers of our model to generate final predictions. Our results show high accuracy, sensitivity, and specificity, indicating that our model performed well on our dataset. Future works can improve upon these results by training on more varied data, classifying tumors based on their grade, and introducing more recent architectural techniques.