Computerized Image Analysis Research Articles

Whole-Kidney Three-Dimensional Staining with CUBIC.

Although conventional pathology provided numerous information about kidney microstructure, it was difficult to know the precise structure of blood vessels, proximal tubules, collecting ducts, glomeruli, and sympathetic nerves in the kidney due to the lack of three-dimensional (3D) information. Optical clearing is a good strategy to overcome this big hurdle. Multiple cells in a whole organ can be analyzed at single-cell resolution by combining tissue clearing and 3D imaging technique. However, cell labeling methods for whole-organ imaging remain underdeveloped. In particular, whole-mount organ staining is challenging because of the difficulty in antibody penetration. The present protocol developed a whole-mount mouse kidney staining for 3D imaging with the CUBIC (Clear, Unobstructed Brain/Body Imaging Cocktails and Computational analysis) tissue clearing method. The protocol has enabled visualizing renal sympathetic denervation after ischemia-reperfusion injury and glomerulomegaly in the early stage of diabetic kidney disease from a comprehensive viewpoint. Thus, this technique can lead to new discoveries in kidney research by providing a macroscopic perspective.

Investigation of heterocellular features of the mouse retinal neurovascular unit by 3D electron microscopy.

The retina has a complex structure with a diverse collection of component cells that work together to facilitate vision. The retinal capillaries supplying the nutritional requirements to the inner retina have an intricate system of neural, glial and vascular elements that interconnect to form the neurovascular unit (NVU). The retina has no autonomic nervous system and so relies on the NVU as an interdependent, physical and functional unit to alter blood flow appropriately to changes in the physiological environment. The importance of this is demonstrated by alterations in NVU function being apparent in the blinding disease diabetic retinopathy and other diseases of the retina. It is, therefore, imperative to understand the anatomy of the components of the NVU that underlie its functioning and in particular the nanoscale arrangements of its heterocellular components. However, information on this in three spatial dimensions is limited. In the present study, we utilised the technique of serial block-face scanning electron microscopy (SBF-SEM), and computational image reconstruction, to enable the first three-dimensional ultrastructural analysis of the NVU in mouse retinal capillaries. Mouse isolated retina was prepared for SBF-SEM and up to 150 serial scanning electron microscopy images (covering z-axes distances of 12-8 mm) of individual capillaries in the superficial plexus and NVU cellular components digitally aligned. Examination of the data in the x-, y- and z-planes was performed with the use of semi-automated computational image analysis tools including segmentation, 3D image reconstruction and quantitation of cell proximities. A prominent feature of the capillary arrangements in 3D was the extensive sheath-like coverage by singular pericytes. They appeared in close register to the basement membrane with which they interwove in a complex mesh-like appearance. Breaks in the basement membrane appeared to facilitate pericyte interactions with other NVU cell types. There were frequent, close (<10nm) pericyte-endothelial interactions with direct contact points and peg-and-socket-like morphology. Macroglia typically intervened between neurons and capillary structures; however, regions were identified where neurons came into closer contact with the basement membrane. A software-generated analysis to assess the morphology of the different cellular components of the NVU, including quantifications of convexity, sphericity and cell-to-cell closeness, has enabled preliminary semi-quantitative characterisation of cell arrangements with neighbouring structures. This study presents new data on the nanoscale spatial characteristics of components of the murine retinal NVU in 3D that has implications for our understanding of structural integrity (e.g. pericyte-endothelial cell anchoring) and function (e.g. possible paracrine communication between macroglia and pericytes). It also serves as a platform to inform future studies examining changes in NVU characteristics with different biological and disease circumstances. All raw and processed image data have been deposited for public viewing.

Vascular Remodeling Is a Crucial Event in the Early Phase of Hepatocarcinogenesis in Rodent Models for Liver Tumorigenesis.

Simple SummaryHepatocellular carcinoma (HCC) is a highly vascularized tumor and remodeling of the tumor vasculature is one of the hallmarks of tumor progression. Mouse models are elegant tools to study the onset and progression of liver tumors. However, only few data exist on the vasculature and vascular remodeling processes especially in the early phase of hepatocarcinogenesis. The aim of this study was therefore to perform a comprehensive characterization and comparison of the vasculature in mouse models used for hepatocarcinogenesis studies. For this purpose, we characterized the preneoplastic foci of cellular alteration (FCA) and hepatocellular carcinoma (HCC) by using tissue-based techniques and computer-assisted analysis to better understand if and how vascular remodeling appears in rodent models for liver tumorigenesis. Our findings demonstrated crucial differences in the number and size of the vessels, degree of maturation and intratumoral localization of the vasculature in FCA and HCC, clearly indicating that vascular remodeling is an important step in the early phase of liver tumorigenesis of rodent models.The investigation of hepatocarcinogenesis is a major field of interest in oncology research and rodent models are commonly used to unravel the pathophysiology of onset and progression of hepatocellular carcinoma. HCC is a highly vascularized tumor and vascular remodeling is one of the hallmarks of tumor progression. To date, only a few detailed data exist about the vasculature and vascular remodeling in rodent models used for hepatocarcinogenesis. In this study, the vasculature of HCC and the preneoplastic foci of alteration (FCA) of different mouse models with varying genetic backgrounds were comprehensively characterized by using immunohistochemistry (CD31, Collagen IV, αSMA, Desmin and LYVE1) and RNA in situ hybridization (VEGF-A). Computational image analysis was performed to evaluate selected parameters including microvessel density, pericyte coverage, vessel size, intratumoral vessel distribution and architecture using the Aperio ImageScope and Definiens software programs. HCC presented with a significantly lower number of vessels, but larger vessel size and increased coverage, leading to a higher degree of maturation, whereas FCA lesions presented with a higher microvessel density and a higher amount of smaller but more immature vessels. Our results clearly demonstrate that vascular remodeling is present and crucial in early stages of experimental hepatocarcinogenesis. In addition, our detailed characterization provides a strong basis for further angiogenesis studies in these experimental models.

Nodular Cast Iron GGG40, 60, 70 Mechanical Characterization from Bars and Blocks Obtained from Brazilian Foundry

Nodular cast iron has been commonly applied in industry and many engineering applications due to its low production cost and the similarity of its mechanical properties to carbon steel. The mechanical properties of nodular cast iron are very dependent on its microstructure and also on the characteristics of the graphite nodules. In this sense, the main objective of this paper was to evaluate and characterize the nodular cast iron grades GGG40, GGG60 and GGG70 in the absence of heat treatment. In addition, specimens were obtained from casted bars and blocks without the Y-block casting process. The microstructure was analyzed by optical microscopy with the support of computational image analysis for determination of the attributes of the graphite nodules and the quantification of each phase present in the microstructure of the nodular cast iron. The results showed that the microstructure has a strong effect on the material’s strength, especially the density of graphite nodules in the material. This difference reinforces the idea that cast iron can undergo mechanical changes due to changes in the casting process, confirming the importance of checking the characteristics of the cast batch before engineering applications of the material.

Elimination of Image Saturation Effects on Multifractal Statistics Using the 2D WTMM Method.

Imaging artifacts such as image saturation can restrict the computational analysis of medical images. Multifractal analyses are typically restricted to self-affine, everywhere singular, surfaces. Image saturation regions in these rough surfaces rob them of these core properties, and their exclusion decreases the statistical power of clinical analyses. By adapting the powerful 2D Wavelet Transform Modulus Maxima (WTMM) multifractal method, we developed a strategy where the image can be partitioned according to its localized response to saturated regions. By eliminating the contribution from those saturated regions to the partition function calculations, we show that the estimation of the multifractal statistics can be correctly calculated even with image saturation levels up to 20% (where 20% is the number of saturated pixels over the total number of pixels in the image).

TECHNIQUES FOR DETERMINING SIZE AND SHAPE OF DRILL CUTTINGS

In the oil and gas industry, the presence of cuttings during the well drilling stage creates a concerning problem known as erosion. The risks of catastrophic failure caused by erosion in hydraulic systems become even greater (e.g., choke line and MPD) when observing control and safety wells on drilling rigs. The geometric characteristics of the particles are important physical attributes, as they influence the wear mechanisms of the materials directly. Therefore, this work presents an analysis of the methodology used for determining the size and shape of particles. Initially, the collection of samples was obtained by Chute Riffling technique. The samples were tested using vibrating sieves for particle size analysis. The study uses computerized image analysis to determine the particle size distribution, as well as shape parameters. The application of both techniques demonstrates that the image analysis results were very similar to the sieving, even when a much smaller sub-sample is under analysis. The granulometric analysis of the diameter of the drill cuttings by the method of vibratory sieving showed a range of size ranging from 0.10 mm to 3.35 mm, and, by the image analysis, it showed a range of size going from 0.11mm to 4.2mm. The shape parameters of the majority of drill cuttings analyzed in the present work was classified as “high circularity” and “well-rounded.”

NEURAL NETWORKS TO UNDERSTAND THE PHYSICS OF ONCOLOGICAL MEDICAL IMAGING

The evolving field of computational image analysis has its applications in the industry, manufacturing and biological sciences, especially in the field of medical imaging. Medical imaging and computational physics have evolved together during the past decades with the advancement in the field of artificial intelligence (AI). Deep learning is the sub-domain of AI that mostly deals with imaging data for classification, segmentation and reconstruction. The time series of medical images of different patients, with different staging are categorized based on the physical and biological consequences. The hypothesis of the current research is that the deep learning tool, if trained on several patients, can identify the stage of cancer swiftly for fresh data sets. During this research, an advance Convolutional Neural Network (CNN) strategy is adopted to classify the cancer stage for a group of patients of gastric cancer. The CNN model makes use of skipping connections for better prediction. CNNs have been quite popular in medical imaging for their ability of feature detection. CNNs are used in the recent literature for the analysis of images. During this research, we have used the state-of-the-art Matlab ResNet CNN toolbox for the analysis of the images obtained from esophageal and gastric cancer patients. It was concluded that RESNET50 is a reliable algorithm for the determination of tumor mass on CT Scans. Moreover, the performance of the model can be improved by giving a comparatively larger data set as an input to the model. Inspired from Caltech101, a logic related to RESNET50 was adopted. The data was processed and an algorithm was designed to develop a mapping, based on the mass of tumor. The algorithm designed successfully identified the images, randomly picked from different patients, based on the image features.

A quantitative study of bone defects in displaced femoral neck fractures based on virtual reduction techniques

BackgroundBone defects in femoral neck fractures are strongly associated with the prognosis after internal fixation. However, qualitative analysis of bone defects in femoral neck fractures has already been performed, quantitative studies have not been reported. In this study, we aimed to systematically analyse the morphological characteristics of bone defects in patients with femoral neck fractures using computed tomography (CT) images combined with computer image analysis techniques. MethodsFour hundred and sixty-nine patients with femoral neck fractures from January 2014 to December 2018 at two grade A tertiary hospitals were included. Models were created in Mimics software based on CT images collected within 1 week after injury and then imported into 3-matic software for virtual reduction. The volume of the bone defect (VBD), maximum defect thickness (MDT), extent of the bone defect region (EBDR) , main defect quadrant (MDQ), collapse type and fracture classification were calculated and recorded. ResultsThe EBDR, collapse type and MDT all had a significant positive effect on the VBD (P <0.05), with a more significant effect at higher quantiles. Age also had a significant positive effect on the VBD (P < 0.05), but its effect was more pronounced at lower quantiles. Compared to non-subcapital fractures, subcapital fractures had a positive effect on the VBD only at the 50 and 75% quantiles (P < 0.01). The female sex had a significant negative effect on the VBD compared to the male sex (P < 0.05). ConclusionThis study established a reliable computer image processing method for quantitative analysis of the VBD in femoral neck fractures and revealed that all patients with femoral neck fractures had bone defects, which can occur at any part of the femoral neck. The EBDR, MDT, collapse type, and patient age and sex were all important risk factors for the extent of the defect and should be taken into account in surgical planning.

Abstract 1235: Presence of TLS and combined high densities of PD-L1+ macrophages &amp; CD8+ T cells predict long-term overall survival for patients with advanced NSCLC treated with durvalumab

Abstract Introduction: Predictive biomarkers of anti‒PD-(L)1 therapies have largely focused on the tumor - T cell axis where tumor cell PD-L1 expression has demonstrated its clinical utility in predicting overall survival (OS) in patients with advanced non-small cell lung cancer (NSCLC). Although, other immune cell subsets were shown to be associated with clinical efficacy, their relative impact and combined effect in predicting improved long-term survival warrant further investigation. Using computational image analysis of multiplex immunofluorescence (mIF) and immunohistochemistry (IHC) immune marker panels, we sought to identify single and combined biomarkers of the tumor immune contexture in association with long-term OS in advanced NSCLC patients treated with Durvalumab. Methods: Pre-treatment tumor samples from advanced NSCLC patients (n = 210) enrolled in durvalumab nonrandomized phase 1/2 trial (10 mg/kg Q2W, CP1108/NCT01693562), were stained using IHC and 6-marker mIF panels to detect markers of immune cells, cell functional state and tertiary lymphoid structure (TLS) (PD-L1, CD8, PD-1, Ki67, CD68, CD20, CD1c, NKp46, CD66b, ICOS, FOXP3). Cell marker density (cells/mm2), distribution and proximity were quantified and analyzed in association with overall survival. Results: Computational image analysis of the tumor immune contexture revealed a greater immune inflamed phenotype, both innate (macrophages, dendritic cells) and adaptive (T and B cells), in NSCLC patients with long OS &amp;gt;2 years compared to those with short OS &amp;lt; 1 year (fold change &amp;gt; 2, p &amp;lt; 0.0001). Patient subgroup with high density of individual immune subsets show a median OS (mOS) of 10-20 months (p &amp;lt; 0.01 high vs. low subgroups) while combined markers of innate and adaptive immune cells show an improved mOS &amp;gt; 2 years (p &amp;lt; 0.001). Specifically, among the key findings, combined biomarkers of CD68+ PD-L1+ macrophages and CD8+ T cells predicts for a significant increase in OS for patients with high vs low marker density (HR = 0.21, 95%CI 0.12 - 0.39, p &amp;lt;10-7; mOS 39.5 months [high], 6.5 months [low]). Whereas, in patients with high density of either single biomarkers CD68+ PD-L1+ macrophage or CD8+ T cells mOS is 20.2 or 18.4 months respectively. Moreover, high density of CD20+ B cells, reflective of presence of TLS, associates with improved OS (mOS NR [high], 11 months [low], p = 0.003). In addition, TLS enriched tumors show an increased level of macrophages expressing PD-L1 in synapsis with CD8+ T cells (activated PD1+ or proliferative Ki67+ T cells). Conclusion: These findings demonstrate the importance of both tertiary lymphoid structure and high pre-existing innate-adaptive immunity in driving long-term overall survival of durvalumab-treated patients with NSCLC and highlight the need for the development of multiparametric predictive biomarkers beyond tumor-T cell axis. Citation Format: Lina Meinecke, Jorge Blando, Thomas Herz, Michael Surace, Thomas Padel, Monica Azqueta Gavaldon, Harald Hessel, Farzad Sekhavati, Megha Saraiya, Anmarie Boutrin, Karma Da Costa, Jaime Rodriguez Canales, Ashok Gupta, Carl Barrett, Zachary Aaron Cooper, Ikbel Achour. Presence of TLS and combined high densities of PD-L1+ macrophages &amp; CD8+ T cells predict long-term overall survival for patients with advanced NSCLC treated with durvalumab [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1235.

Alternaria tenuissima, a biodeteriogenic filamentous fungus from ancient Oplontis ruins, rapidly penetrates tuff stone in an in vitro colonization test

To integrate the understanding of mycological biodeterioration on artistic-historical heritage, we cultivated Alternaria tenuissima on Neapolitan Yellow Tuff (NYT) tiles for 20 d in a low-carbon environment. The fungus, collected within the UNESCO site of ancient Oplontis ruins (Naples, Italy), was monitored during its early colonization both for the epilithic and the cryptoendolithic growth by using photomicrography-based image analyses. The data were integrated with the count of conidia and the analysis of biochemical proxies in order to evaluate the biological status of the fungus during growth. Additionally, we provided novel and synthetic insights on A. tenuissima 3D growth dynamics, suggesting that visible fungal spots on stone monuments could represent the “iceberg tip” for advanced stone decay. Our results confirm that in vitro tests, coupled with microscopical observations and computer image analysis, are useful tools to evaluate and quantify fungal biomass on a stone substratum, especially in the early steps of fungal colonization.

A state-of-the-art survey of object detection techniques in microorganism image analysis: from classical methods to deep learning approaches.

Microorganisms play a vital role in human life. Therefore, microorganism detection is of great significance to human beings. However, the traditional manual microscopic detection methods have the disadvantages of long detection cycle, low detection accuracy in large orders, and great difficulty in detecting uncommon microorganisms. Therefore, it is meaningful to apply computer image analysis technology to the field of microorganism detection. Computer image analysis can realize high-precision and high-efficiency detection of microorganisms. In this review, first,we analyse the existing microorganism detection methods in chronological order, from traditional image processing and traditional machine learning to deep learning methods. Then, we analyze and summarize these existing methods and introduce some potential methods, including visual transformers. In the end, the future development direction and challenges of microorganism detection are discussed. In general, we have summarized 142 related technical papers from 1985 to the present. This review will help researchers have a more comprehensive understanding of the development process, research status, and future trends in the field of microorganism detection and provide a reference for researchers in other fields.

Monitoring of Nitrogen Transport Data in Pear Leaves Based on Infrared Spectroscopy

In order to better monitor the data of nitrogen transport in pear leaves, a method based on infrared spectroscopy was proposed. The near-infrared reflection spectrum imaging technology is used to collect the leaf scale spectral image of the target crop. Computer image analysis software is used to process the spectral digital image and extract the spectral data. After statistical analysis, the data are selected as variables. Combined with the chemical analysis test results, the crop nutrition detection model is established, and the conclusion is drawn. The experimental results show that the band gray data involved in the model are scaled and reorganized according to the coefficient proportion by using ENVI through the band calculation command. The final gray image, the original image, and the gray image in the process default to the three-channel analog image of the band (the wavelengths of the bands are 1446, 1373, and 1304 nm, respectively); 944 nm gray image; 1043 nm gray scale image; 1662 nm gray image; (0.102R944 +0.103R1 043 +0.206R1662)/(0.102 + 0.103 + 0.206) grayscale image with signal scaling according to the scale of model coefficient. It is proved that infrared spectroscopy can effectively monitor the data of nitrogen transport in pear leaves.

Automated Crack Detection With Image Analysis for the Blades of Steam Turbine

Abstract Blades are a critical part of steam turbines. Since they usually work under extremely harsh conditions, it is necessary to detect cracks that are generated during operation in time and prevent them from developing into larger ones. Crack detection is crucial to maintaining the structural health and operational safety of steam turbines. Today, one of the most common detection methods is to perform magnetic particle flaw detection manually, but it is subject to the subjective judgment of inspectors, with a low level of automation. This paper presents an automated crack detection device, which can perform magnetic particle inspection on the blades and transfer images to a host computer for further image analysis. After comparing the performance of different object detection models, yolov4 (you only look once—version 4), which is a fast and accurate real-time object detection algorithm, is chosen in this paper to extract subimages containing cracks on the host computer. Furthermore, an intelligent crack detection model is established from image processing techniques, which can be divided into four steps: image preprocessing, edge detection, crack extraction and crack length calculation. In the step of image preprocessing, a new image pyramid method is proposed to blur the background and eliminate the texture of the metal surface while keeping the cracks' information to the utmost extent. An experimental study shows a reliable performance of the proposed crack detection model.

Automatic Real-Estate Image Analysis for Retrieval and Classification

Abstract Nowadays, the real estate sector is also benefiting from the advance of the technology. Digital images accompany announcements advertising for renting or selling a property. Computer vision and image analysis contribute to the understanding of these images towards better consumer experience. In this paper we show how retrieval and description can be used efficiently to isolate cvasi-duplicate images and identify similar examples to the ones that have drawn viewer’s interest. Three problems related to real-estate imaging are identified and solutions are proposed and evaluated on large databases.

Density patterns of tumor-infiltrating lymphocytes and association with objective response to nivolumab in patients with lung adenocarcinoma from CheckMate 057.

2662 Background: Immune checkpoint inhibitors (ICIs) are currently approved for use as therapy in advanced stage lung adenocarcinoma (LUAD). ICIs can decrease risk of progression by up to 60% when compared to chemotherapy, but only about 20% of patients (pts) show response. Given that high levels of tumor-infiltrating lymphocytes (TILs) have been shown to be associated with better prognosis, here, we assess whether computationally derived TIL density measures on digitized H&amp;E images can predict RECIST derived response to nivolumab in LUAD in Checkmate 057 (CM057). CM057 is a clinical trial designed to compare the overall survival of metastatic non-squamous non-small cell lung cancer subjects treated with either nivolumab or docetaxel after failing platinum-based chemotherapy. Methods: H&amp;E-stained samples of 683 LUAD pts were collected from TCGA (n = 421), University of Bern (UBern) (n = 100), and CM057 (n = 162). Tumor response was assessed using RECIST v1.1. Samples were digitized as whole slide images. 294 pts randomly selected from TCGA formed the training set. The remaining 389 pts were used for validation in response to nivolumab in CM057 and prognosticating overall survival (OS) in UBern and TCGA. Computerized algorithms automatically identified TILs and extracted features related to quantity and compactness of TILs with respect to other surrounding nuclei. The top 6 features, determined by least absolute shrinkage and selection operator, were used to train Cox regression models that assign a death risk score to each patient. Pts with risk scores higher than the training median score were considered “high risk” or “non-responders” while pts with lower scores were considered “low risk” or “responders”. Results: The classifier predicted objective response in CM057 with an AUC = 0.61. Additionally, survival analysis showed that the model was prognostic for OS with hazard ratios of 2.38 (confidence interval (CI): 1.32-4.29, p = 0.01, n = 127) in TCGA and 2.37 (CI: 1.32-4.25, p &lt; 0.01, n = 100) in UBern. Conclusions: A computerized image analysis model based on measurements of TIL density showed association with response to treatment in LUAD pts who received nivolumab and was prognostic of OS. Although the AUC was not high, the results suggest analysis of TILs has potential to identify pts who will respond to treatment. Future work will include training a classifier using response to treatment as endpoint and combining the TIL measures with other biomarkers like TMB or PD-L1.

An Immune-metabolic signature (IMMETCOLS) identifies three clusters in mCRC with different immune-phenotype distribution and potential clinical implications.

e15534 Background: The overall response rate following immune check-point blockade (ICB) is &lt; 5% in microsatellite stable (MSS) advanced colorectal cancer (mCRC), and between 30-35% in MSI mCRC. We here evaluate the relationship between our immune-metabolic signature (IMMETCOLS) distribution (cluster (IMC) 1; mesenchymal glycolytic, IMC 2; epithelial-non glycolytic and IMC 3; epithelial mitochondrial oxidative) with immune phenotypes (inflamed (IF), immune-excluded (IE) and immune-desert (ID)) in MSS and MSI mCRC patients (pts). Methods: We have analyzed IMMETCOLS in a retrospective cohort of 128 mCRC pts enriched with MSI-H and BRAF genotype using the nCounter platform (Nanostring Technologies, US). Immune phenotypes were evaluated by immunohistochemistry using 7 immune markers (CD68, CD163, CD8, CD3, FOXP3, PD-L1 and PD1) in a subset of 47 mCRC pts (27 MSS and 20 MSI), in TMAs containing 4 cores from the center of the tumor (CT) and invasive front (IF). The number of stained immune cells was counted using a computerized image analysis system, and PD-L1 was evaluated using CPS score (&lt; 1 vs &gt; 1). Patients having a CD3 cell count below 572 cells/mm2 and a CD8 density below 175 cells/mm2 in IF or CT were considered as ID. (Halama Nils, 2011). CD3 density was also semi-quantitatively estimated in peritumoral stroma and intra-tumoral (range 1-4), defining as IE those cases with a difference &gt; 1 between the two regions in both IF and CT (Brooks JM, 2019). Results: Cluster distribution was similar between MSI (IMC1-40%, IMC2-20% and IMC-40%) and MSS (IMC1-34%, IMC2-24%, IMC3-43%) pts. Patients with high LDH levels (&gt; 1.5ULN) were enriched with IMC3 phenotype (p &lt; 0.0001). MSI have increased CD3 (p = 0.002), CD68 (p = 0.002) and CD163 (p = 0.001) in CT than MSS patients. IMC1 pts had increased CD163/CD68 ratio in CT (p = 0.004). 75% of MSI pts were IE and 25% IF. 55% of MSS pts were IE, 38% ID and 7% IF. IMMETCOLS/ immune-phenotype distribution was: IMC1 (16% ID/ 84% IE), IMC2 (12% ID/ 63% IE/ 25% IF) and IMC3 (35%ID/ 47% IE/ 18% IF). PD1 immuno-expression &gt; 1% (IF and CT) and PD-L1 positivity was observed in 38%, 30% and 28% of pts respectively. There were no differences in PD-L1 (CPS) and PD1 expression between MSS and MSI tumors. Inflamed tumors show higher PD-L1 expression compared with IE and ID immune-phenotypes (p = 0.014). PD1 &gt; 1% expression at IF was significatively lower in IMC1 vs IMC2 and IMC3 tumors (p = 0.032). Conclusions: IMC1 subtype is associated with low PD1 expression, high CD163/CD68 ratio and IE phenotype even in MSI-H mCRC patients. Therefore, IMMETCOLS testing would be of value to identify MSI mCRC pts that do not benefit to ICB therapy.

PodoCount: A Robust, Fully Automated, Whole-Slide Podocyte Quantification Tool

IntroductionPodocyte depletion is a histomorphologic indicator of glomerular injury and predicts clinical outcomes. Podocyte estimation methods or podometrics are semiquantitative, technically involved, and laborious. Implementation of high-throughput podometrics in experimental and clinical workflows necessitates an automated podometrics pipeline. Recognizing that computational image analysis offers a robust approach to study cell and tissue structure, we developed and validated PodoCount (a computational tool for automated podocyte quantification in immunohistochemically labeled tissues) using a diverse data set.MethodsWhole-slide images (WSIs) of tissues immunostained with a podocyte nuclear marker and periodic acid–Schiff counterstain were acquired. The data set consisted of murine whole kidney sections (n = 135) from 6 disease models and human kidney biopsy specimens from patients with diabetic nephropathy (DN) (n = 45). Within segmented glomeruli, podocytes were extracted and image analysis was applied to compute measures of podocyte depletion and nuclear morphometry. Computational performance evaluation and statistical testing were performed to validate podometric and associated image features. PodoCount was disbursed as an open-source, cloud-based computational tool.ResultsPodoCount produced highly accurate podocyte quantification when benchmarked against existing methods. Podocyte nuclear profiles were identified with 0.98 accuracy and segmented with 0.85 sensitivity and 0.99 specificity. Errors in podocyte count were bounded by 1 podocyte per glomerulus. Podocyte-specific image features were found to be significant predictors of disease state, proteinuria, and clinical outcome.ConclusionPodoCount offers high-performance podocyte quantitation in diverse murine disease models and in human kidney biopsy specimens. Resultant features offer significant correlation with associated metadata and outcome. Our cloud-based tool will provide end users with a standardized approach for automated podometrics from gigapixel-sized WSIs.

0301 Does Orexin Influence Sleep Fragmented Brain Inflammation? A Pilot Study

Abstract Introduction Fragmented sleep occurs when there are repetitive, short interruptions of sleep, resulting in less than six hours of sleep per day. Overall, however, in the United States, sleep fragmentation is reported by 30% of employed adults. Sleep fragmentation may be a risk factor for Alzheimer's disease, and orexin reduction appears effective in reducing amyloid plaque formation in mice with transgenic Alzheimer's disease. Orexin is a neuropeptide that regulates arousal, wakefulness, and appetite. Thus we hypothesized that loss of orexin might also be protective against sleep disruption injury in non-Alzheimer's mice. This study aims to combine sleep fragmentation with orexin loss to see if brain health is severely reduced. Methods The 24 mice in this study were split into four groups: B6 rested, B6 sleep fragmented, orexin knockout rested, orexin knockout sleep fragmented. The sleep fragmented mice were placed on a shaker table for 10½ weeks to initiate chronic sleep loss. The mice were all perfused within five to eight months of birth, and then the brains were cryopreserved and sliced. These sections were immunolabeled with different protein antibodies using immunohistochemistry techniques. The stained brains were either analyzed through microscope stereology counts or computer image analysis. Results Two-way ANOVA analysis for tyrosine hydroxylase, ionized calcium binding adaptor, and vesicular acetylcholine transporter had p&amp;lt;0.05 for the sleep fragmentation variable, showing differences in these antibodies for rested and sleep loss mice. ANOVA for cluster of differentiation 68, cofilin, postsynaptic density protein, and RanBP had p&amp;lt;0.05 for the genotype variable, showing differences in these antibodies for knockout and normal orexin mice. ANOVA for glial fibrillary acidic protein and amyloid-beta had p&amp;lt;0.05 for both variables, showing differences for sleep and orexin levels. There was no ANOVA significance for synapsin. Conclusion Our results show that knocking out orexinergic neurons causes hippocampal tissue damage, dampens the functioning of synapses, and diminishes the ability of the brain to adapt through plasticity and memory. Sleep fragmentation, however, increases phagocytic activity, and harms the acetylcholine and norepinepherine neurotransmitter pathways. When combined, cell communication worsens and the blood brain barrier loses function, resemblant of neurodegenerative diseases. Support (If Any) NIH AG054104; AG064231

Deciphering the Immune Complexity in Esophageal Adenocarcinoma and Pre-Cancerous Lesions With Sequential Multiplex Immunohistochemistry and Sparse Subspace Clustering Approach.

Esophageal adenocarcinoma (EAC) develops from a chronic inflammatory environment across four stages: intestinal metaplasia, known as Barrett’s esophagus, low- and high-grade dysplasia, and adenocarcinoma. Although the genomic characteristics of this progression have been well defined via large-scale DNA sequencing, the dynamics of various immune cell subsets and their spatial interactions in their tumor microenvironment remain unclear. Here, we applied a sequential multiplex immunohistochemistry (mIHC) platform with computational image analysis pipelines that allow for the detection of 10 biomarkers in one formalin-fixed paraffin-embedded (FFPE) tissue section. Using this platform and quantitative image analytics, we studied changes in the immune landscape during disease progression based on 40 normal and diseased areas from endoscopic mucosal resection specimens of chemotherapy treatment- naïve patients, including normal esophagus, metaplasia, low- and high-grade dysplasia, and adenocarcinoma. The results revealed a steady increase of FOXP3+ T regulatory cells and a CD163+ myelomonocytic cell subset. In parallel to the manual gating strategy applied for cell phenotyping, we also adopted a sparse subspace clustering (SSC) algorithm allowing the automated cell phenotyping of mIHC-based single-cell data. The algorithm successfully identified comparable cell types, along with significantly enriched FOXP3 T regulatory cells and CD163+ myelomonocytic cells as found in manual gating. In addition, SCC identified a new CSF1R+CD1C+ myeloid lineage, which not only was previously unknown in this disease but also increases with advancing disease stages. This study revealed immune dynamics in EAC progression and highlighted the potential application of a new multiplex imaging platform, combined with computational image analysis on routine clinical FFPE sections, to investigate complex immune populations in tumor ecosystems.

Abstract B020: Distinct spatiotemporal tumor-immune ecologies enable disease prediction in NSCLC patients

Abstract Purpose of study: Non-small cell lung cancer (NSCLC) is the most common and fatal of cancers. In this work, we examine multiplexed images of NSCLC tumors to investigate both the spatial and spatiotemporal eco-evolutionary interactions between the tumor and its microenvironment, to better understand NSCLC tumor progression and therapy response. Methods: We developed a scalable, computational image analysis pipeline using cell-segmentation and quadrat-based approaches to analyze the spatial and temporal features of high-dimensional multiplexed NSCLC images. Multiplexed images enable the spatial readouts of numerous biomarkers per tissue sample and allow the interpretation of cellular states and the characterization of tumor-immune interactions across tissue ensembles. We also implement statistical approaches for ecological niche modelling combined with machine learning and deep learning models to predict disease progression and identify clinical imaging biomarkers. Data: Images were obtained from two 9-patient cohorts having advanced/metastatic NSCLC who were treated with the oral HDAC inhibitor vorinostat combined with the PD-1 inhibitor pembrolizumab. The first cohort had 4 progressors (PD) and 5 with stable disease (SD). The second cohort had 3 patients each in the PD, SD and partial response (PR) categories. Images were collected from all patients both pre- and on-treatment (during the third week). Results: Using our computational framework based on cell segments and quadrats, we confirm that different spatial neighborhoods exist that distinguish PD from SD, and that these spatial ecologies aid disease progression: PD patients have distinct ecologies with higher colocalization of PanCK+PD-1+FoxP3 indicating an immunosuppressive environment, whereas SD patients have a higher colocalization of PanCK+PD-L1 along with T cells, suggesting immunoactive tumor regions. These can be considered as potential biomarker candidates for predicting tumor progression. In an additional experiment where we include PR samples in our analyses, these distinct spatial neighborhoods are reinforced amongst PD, SD and PR patient groups corroborating the existence of spatiotemporal patterns. Further, we were able to predict treatment response with &amp;gt;91% accuracy given each patient’s spatial distribution of cell types from pre-treatment images. Using these predictions, we can generate risk maps at the patient level to identify areas of the tumor that are indicators of a higher probability of progression during treatment. Conclusions: We leveraged both single-cell and quadrat-resolution analysis of multiplexed imaging data and identified fundamentally distinct spatial ecologies between PD and SD patients. The ecology in PD patients appears to be primed for immune resistance even before treatment. This ecological diversity between SD and PD patients acts as a biomarker that enables accurate disease progression prediction. Our disease progression predictions can be used in conjunction with standard PD-L1 status to further strengthen personalized treatment strategies. Citation Format: Sandhya Prabhakaran, Chandler Gatenbee, Mark Robertson-Tessi, Amer A. Beg, Jhanelle Gray, Scott Antonia, Robert A. Gatenby, Alexander R.A. Anderson. Distinct spatiotemporal tumor-immune ecologies enable disease prediction in NSCLC patients [abstract]. In: Proceedings of the AACR Special Conference on the Evolutionary Dynamics in Carcinogenesis and Response to Therapy; 2022 Mar 14-17. Philadelphia (PA): AACR; Cancer Res 2022;82(10 Suppl):Abstract nr B020.