SUMMARY Beyond sample curation and basic pathologic characterization, the digitized H&E-stained images of TCGA samples remain underutilized. To highlight this resource, we present mappings of tumor-infiltrating lymphocytes (TILs) based on H&E images from 13 TCGA tumor types. These TIL maps are derived through computational staining using a convolutional neural network trained to classify patches of images. Affinity propagation revealed local spatial structure in TIL patterns and correlation with overall survival. TIL map structural patterns were grouped using standard histopathological parameters. These patterns are enriched in particular T cell subpopulations derived from molecular measures. TIL densities and spatial structure were differentially enriched among tumor types, immune subtypes, and tumor molecular subtypes, implying that spatial infiltrate state could reflect particular tumor cell aberration states. Obtaining spatial lymphocytic patterns linked to the rich genomic characterization of TCGA samples demonstrates one use for the TCGA image archives with insights into the tumor-immune microenvironment.

Abstract. The basic parameters for physiological measurements provided by near-infrared spectroscopy are the local absorption and scattering coefficients. For the adult human head, they have been difficult to measure noninvasively because of the layered structure of the head. The results of measurements of absorption and reduced scattering coefficients through the forehead on 30 adult volunteers using a multidistance frequency domain method are reported. The optode separation distance ranged from 10 to 80 mm and measurements were recorded at 758 and 830 nm. The measured absorption and reduced scattering coefficients of the forehead were used to evaluate the hemoglobin content in the scalp and brain as well as cerebral oxygen saturation. We found that cerebral oxygenation was relatively narrowly distributed within the subject group (the standard deviation was about 3% for scalp and 6% for brain, respectively), whereas hemoglobin concentrations had a relatively broader distribution. We found that as the optode distance increased, the absorption coefficients increased and the scattering coefficients decreased, retrieving the optical values of scalp and brain for shorter and longer optode distances, respectively. We present the transition curves of the absorption and reduced scattering coefficients as functions of the optode distance. In order to verify the values for each layer, a comparison between the experimental data and a prediction based on the two-layer model of the adult head was carried out. The thicknesses of scalp and skull for the two-layer model were obtained by magnetic resonance imaging of a subject's head. The optical parameters obtained from the two-layer model agreed very well with those measured by the multidistance method.

High-resolution microscopy images of tissue specimens provide detailed information about the morphology of normal and diseased tissue. Image analysis of tissue morphology can help cancer researchers develop a better understanding of cancer biology. Segmentation of nuclei and classification of tissue images are two common tasks in tissue image analysis. Development of accurate and efficient algorithms for these tasks is a challenging problem because of the complexity of tissue morphology and tumor heterogeneity. In this paper we present two computer algorithms; one designed for segmentation of nuclei and the other for classification of whole slide tissue images. The segmentation algorithm implements a multiscale deep residual aggregation network to accurately segment nuclear material and then separate clumped nuclei into individual nuclei. The classification algorithm initially carries out patch-level classification via a deep learning method, then patch-level statistical and morphological features are used as input to a random forest regression model for whole slide image classification. The segmentation and classification algorithms were evaluated in the MICCAI 2017 Digital Pathology challenge. The segmentation algorithm achieved an accuracy score of 0.78. The classification algorithm achieved an accuracy score of 0.81. These scores were the highest in the challenge.

Necrotizing fasciitis is a medical emergency with potential lethal outcome. Dissecting gas along fascial planes in the absence of penetrating trauma (including iatrogenic) is essentially pathognomonic. However, the lack of soft-tissue emphysema does not exclude the diagnosis. Mimics of necrotizing fasciitis include nonnecrotizing fasciitis (eosinophilic, paraneoplastic, inflammatory (lupus myofasciitis, Churg-Strauss, nodular, or proliferative), myositis, neoplasm, myonecrosis, inflammatory myopathy, and compartment syndrome. Necrotizing fasciitis is a clinical diagnosis, and imaging can reveal nonspecific or negative findings (particularly during the early course of disease). One should be familiar with salient clinical and imaging findings of necrotizing fasciitis to facilitate a more rapid and accurate diagnosis and be aware that its diagnosis necessitates immediate discussion with the referring physician.

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