Spatial analyses of immune cell infiltration in cancer : current methods and future directions. A report of the International Immuno-Oncology Biomarker Working Group on Breast Cancer

Modern histologic imaging platforms coupled with machine learning methods have provided new opportunities to map the spatial distribution of immune cells in the tumor microenvironment. However, there exists no standardized method for describing or analyzing spatial immune cell data, and most reported spatial analyses are rudimentary. In this review, we provide an overview of two approaches for reporting and analyzing spatial data (raster versus vector-based). We then provide a compendium of spatial immune cell metrics that have been reported in the literature, summarizing prognostic associations in the context of a variety of cancers. We conclude by discussing two well-described clinical biomarkers, the breast cancer stromal tumor infiltrating lymphocytes score and the colon cancer Immunoscore, and describe investigative opportunities to improve clinical utility of these spatial biomarkers. © 2023 The Pathological Society of Great Britain and Ireland.http://www.thejournalofpathology.com/hj2024ImmunologySDG-03:Good heatlh and well-bein

DETECÇÃO E DIAGNÓSTICO DE MASSAS EM MAMOGRAFIA: revisão bibliográfica

Resumo: O câncer de mama tem se tornado cada dia mais freqüente entre a população feminina acima dos 40 anos. Somente para o ano de 2011 são estimados, no Brasil, 49 mil novos casos. Uma das maneiras para detectar os tumores não palpáveis que causam câncer de mama é realizar uma radiografia (mamografia) das mamas. A  mamografia é atualmente a melhor técnica de detecção precoce de lesões não apalpáveis na mama com altas chances de ser um câncer curável. Sabe-se que as chances de cura do câncer de mama são, relativamente altas, se detectado nos estágios inicias. Entretanto, a sensibilidade desse exame pode variar bastante, em decorrência de fatores como qualidade do exame ou experiência do especialista. Dessa forma, a utilização de sistemas CAD e CADx tem contribuído para aumentar as chances de uma detecção e diagnósticos corretos, ou seja, uma segunda opinião, auxiliando os especialistas na tomada de decisões em um tratamento do câncer de mama. Este artigo faz uma revisão bibliográfica de trabalhos voltados para detecção e diagnóstico de massas.Palavras-chave: Massa. Mamografia. Detecção. Diagnóstico. Câncer de mama.MAMMOGRAPHY MASS DETECTION AND DIAGNOSIS: a surveyAbstract: Breast cancer has become increasingly common among the female population over 40 years old. Only for the year 2011 are estimated, in Brazil, 49 000 new cases. One way to detect non-palpable tumors that cause breast cancer is to perform an X-ray (mammogram) of the breasts. Mammography is currently the best technique for early detection of non-palpable breast lesions with high chances of being a curable cancer. It is known that the chances of a cure for breast cancer are relatively high if detected in early stages. However, the sensitivity of this exam can vary greatly due to factors such as quality of examination or experience of the specialist. Thus, the use of CAD systems and CADX has contributed to increase the chances of detection and correct diagnosis, working as a second opinion in treatment of breast cancer. This article is a literature review of studies focused on detection and diagnosis of masses.Keywords: Mass. Mammography. Detection. Diagnosis. Breast cancer.DETECCIÓN Y DIAGNÓSTICO DE MASAS EN UNA MAMOGRAFÍA: una revisión de la literatura Resumen: El cáncer de mama se ha tornado cada vez más común entre la población femenina de más de 40 años. Sólo para el año 2011 se estima que en Brasil habrán 49 000 nuevos casos. Una forma de detectar tumores no palpables que causan el cáncer de mama es realizar una radiografía (mamografía) de los senos. La mamografía es actualmente la mejor técnica para la detección precoz de lesiones mamarias no palpables, con altas posibilidades de ser un cáncer curable. Se sabe que las posibilidades de una cura para el cáncer de mama son relativamente altas si se detecta en etapas tempranas. Sin embargo, la sensibilidad de esta prueba pueden variar considerablemente debido a factores como la calidad de los exámenes o la experiencia del especialista. Por lo tanto, el uso de sistemas CAD y CADX ha contribuido a aumentar las posibilidades de  detección y el diagnóstico correcto, o una segunda opinión, ayudando a los expertos en la tomada de decisiones en el tratamiento del cáncer de mama. Este artículo es una revisión de la literatura de trabajos sobre detección y diagnóstico de masas.Palabras clave: Masa. Mamografía. Detección. Diagnóstico de cáncer de mama

Spatial statistics is a comprehensive tool for quantifying cell neighbor relationships and biological processes via tissue image analysis

Automated microscopy and computational image analysis has transformed cell biology, providing quantitative, spatially resolved information on cells and their constituent molecules from the sub-micron to the whole-organ scale. Here we explore the application of spatial statistics to the cellular relationships within tissue microscopy data and discuss how spatial statistics offers cytometry a powerful yet underused mathematical tool set for which the required data are readily captured using standard protocols and microscopy equipment. We also highlight the often-overlooked need to carefully consider the structural heterogeneity of tissues in terms of the applicability of different statistical measures and their accuracy and demonstrate how spatial analyses offer a great deal more than just basic quantification of biological variance. Ultimately, we highlight how statistical modeling can help reveal the hierarchical spatial processes that connect the properties of individual cells to the establishment of biological function

Integration of Spatial and Spectral Information for Hyperspectral Image Classification

Hyperspectral imaging has become a powerful tool in biomedical and agriculture fields in the recent years and the interest amongst researchers has increased immensely. Hyperspectral imaging combines conventional imaging and spectroscopy to acquire both spatial and spectral information from an object. Consequently, a hyperspectral image data contains not only spectral information of objects, but also the spatial arrangement of objects. Information captured in neighboring locations may provide useful supplementary knowledge for analysis. Therefore, this dissertation investigates the integration of information from both the spectral and spatial domains to enhance hyperspectral image classification performance. The major impediment to the combined spatial and spectral approach is that most spatial methods were only developed for single image band. Based on the traditional singleimage based local Geary measure, this dissertation successfully proposes a Multidimensional Local Spatial Autocorrelation (MLSA) for hyperspectral image data. Based on the proposed spatial measure, this research work develops a collaborative band selection strategy that combines both the spectral separability measure (divergence) and spatial homogeneity measure (MLSA) for hyperspectral band selection task. In order to calculate the divergence more efficiently, a set of recursive equations for the calculation of divergence with an additional band is derived to overcome the computational restrictions. Moreover, this dissertation proposes a collaborative classification method which integrates the spectral distance and spatial autocorrelation during the decision-making process. Therefore, this method fully utilizes the spatial-spectral relationships inherent in the data, and thus improves the classification performance. In addition, the usefulness of the proposed band selection and classification method is evaluated with four case studies. The case studies include detection and identification of tumor on poultry carcasses, fecal on apple surface, cancer on mouse skin and crop in agricultural filed using hyperspectral imagery. Through the case studies, the performances of the proposed methods are assessed. It clearly shows the necessity and efficiency of integrating spatial information for hyperspectral image processing

Using Synchronized Audio Mapping to Predict Velar and Pharyngeal Wall Locations during Dynamic MRI Sequences

Automatic tongue, velum (i.e., soft palate), and pharyngeal movement tracking systems provide a significant benefit for the analysis of dynamic speech movements. Studies have been conducted using ultrasound, x-ray, and Magnetic Resonance Images (MRI) to examine the dynamic nature of the articulators during speech. Simulating the movement of the tongue, velum, and pharynx is often limited by image segmentation obstacles, where, movements of the velar structures are segmented through manual tracking. These methods are extremely time-consuming, coupled with inherent noise, motion artifacts, air interfaces, and refractions often complicate the process of computer-based automatic tracking. Furthermore, image segmentation and processing techniques of velopharyngeal structures often suffer from leakage issues related to the poor image quality of the MRI and the lack of recognizable boundaries between the velum and pharynx during contact moments. Computer-based tracking algorithms are developed to overcome these disadvantages by utilizing machine learning techniques and corresponding speech signals that may be considered prior information. The purpose of this study is to illustrate a methodology to track the velum and pharynx from a MRI sequence using the Hidden Markov Model (HMM) and Mel-Frequency Cepstral Coefficients (MFCC) by analyzing the corresponding audio signals. Auditory models such as MFCC have been widely used in Automatic Speech Recognition (ASR) systems. Our method uses customized version of the traditional approach for audio feature extraction in order to extract visual feature from the outer boundaries of the velum and the pharynx marked (selected pixel) by a novel method, The reduced audio features helps to shrink the search space of HMM and improve the system performance.   Three hundred consecutive images were tagged by the researcher. Two hundred of these images and the corresponding audio features (5 seconds) were used to train the HMM and a 2.5 second long audio file was used to test the model. The error rate was measured by calculating minimum distance between predicted and actual markers. Our model was able to track and animate dynamic articulators during the speech process in real-time with an overall accuracy of 81% considering one pixel threshold. The predicted markers (pixels) indicated the segmented structures, even though the contours of contacted areas were fuzzy and unrecognizable.  M.S

Geospatial immune variability illuminates differential evolution of lung adenocarcinoma

Remarkable progress in molecular analyses has improved our understanding of the evolution of cancer cells toward immune escape. However, the spatial configurations of immune and stromal cells, which may shed light on the evolution of immune escape across tumor geographical locations, remain unaddressed. We integrated multiregion exome and RNA-sequencing (RNA-seq) data with spatial histology mapped by deep learning in 100 patients with non-small cell lung cancer from the TRACERx cohort. Cancer subclones derived from immune cold regions were more closely related in mutation space, diversifying more recently than subclones from immune hot regions. In TRACERx and in an independent multisample cohort of 970 patients with lung adenocarcinoma, tumors with more than one immune cold region had a higher risk of relapse, independently of tumor size, stage and number of samples per patient. In lung adenocarcinoma, but not lung squamous cell carcinoma, geometrical irregularity and complexity of the cancer–stromal cell interface significantly increased in tumor regions without disruption of antigen presentation. Decreased lymphocyte accumulation in adjacent stroma was observed in tumors with low clonal neoantigen burden. Collectively, immune geospatial variability elucidates tumor ecological constraints that may shape the emergence of immune-evading subclones and aggressive clinical phenotypes

Novel insights into Mediterranean forest structure using high-resolution remote sensing.

PhD Theses.Tree crown morphology and arrangement in three-dimensional space is a key driver of forest dynamics, determining not only the competitiveness of an individual but also the competitive effect exerted on neighbouring trees. Many theoretical frameworks aim to predict crown morphology from first principles and assumptions of Euclidean form and ultimately infer whole forest stand structure and dynamics but paucity in data has limited vigorous testing. Tree crowns are also not rigid in form and due to their sessile nature, must morphologically adapt to immediate abiotic and biotic surroundings to enhance survival. The characterisation of tree structure has been limited by the simplicity and associated error of traditional crown measurements. This project uses Terrestrial Laser Scanning data collected from a water limited Mediterranean forest community in Spain to highlight methodological opportunities presented by TLS in understanding forest structure and also the various developments required to extract ecologically meaningful metrics from these data. It then applies these novel metrics to answer questions about how tree crowns scale with size, the effects of competition and how plasticity in shape and arrangement interacts with light capture at the individual and plot scales. Modification to existing code as well as bespoke development were required to segment and calculate individual metrics from trees in this forest type. Accurate measures of crown morphology highlighted allometric scaling deviations from theoretical predictions and intra-specific differences in response to competition, calculated using more representative neighbourhood metrics. Inter-specific differences in crown plasticity and significant effects of size (height) were also evident, along with trade-offs between morphological plasticity and crown size. Light capture was positively affected by plasticity with inter-specific differences highlighting various biomass allocations strategies species undertake to acquire light. At the plot scale, mixed-genus plots intercepted less direct light and were structurally more complex rather than more volume filling

Microenvironmental niche divergence shapes BRCA1-dysregulated ovarian cancer morphological plasticity.

How tumor microenvironmental forces shape plasticity of cancer cell morphology is poorly understood. Here, we conduct automated histology image and spatial statistical analyses in 514 high grade serous ovarian samples to define cancer morphological diversification within the spatial context of the microenvironment. Tumor spatial zones, where cancer cell nuclei diversify in shape, are mapped in each tumor. Integration of this spatially explicit analysis with omics and clinical data reveals a relationship between morphological diversification and the dysregulation of DNA repair, loss of nuclear integrity, and increased disease mortality. Within the Immunoreactive subtype, spatial analysis further reveals significantly lower lymphocytic infiltration within diversified zones compared with other tumor zones, suggesting that even immune-hot tumors contain cells capable of immune escape. Our findings support a model whereby a subpopulation of morphologically plastic cancer cells with dysregulated DNA repair promotes ovarian cancer progression through positive selection by immune evasion

Remote Sensing in Applications of Geoinformation

Remote sensing, especially from satellites, is a source of invaluable data which can be used to generate synoptic information for virtually all parts of the Earth, including the atmosphere, land, and ocean. In the last few decades, such data have evolved as a basis for accurate information about the Earth, leading to a wealth of geoscientific analysis focusing on diverse applications. Geoinformation systems based on remote sensing are increasingly becoming an integral part of the current information and communication society. The integration of remote sensing and geoinformation essentially involves combining data provided from both, in a consistent and sensible manner. This process has been accelerated by technologically advanced tools and methods for remote sensing data access and integration, paving the way for scientific advances in a broadening range of remote sensing exploitations in applications of geoinformation. This volume hosts original research focusing on the exploitation of remote sensing in applications of geoinformation. The emphasis is on a wide range of applications, such as the mapping of soil nutrients, detection of plastic litter in oceans, urban microclimate, seafloor morphology, urban forest ecosystems, real estate appraisal, inundation mapping, and solar potential analysis

Mathematical modelling of fibroblasts in cancer

Cancer-associated fibroblasts (CAFs) and the associated extracellular matrix (ECM) constitute a significant part of the tumour microenvironment (TME), playing an important role in the invasive potential of the tumour. The alignment of CAFs and the corresponding ECM which they produce and organise is linked with increased cancer invasion. Additionally, massive variation in the physical architecture of the ECM is observed in both normal and pathological tissues for example swirling, diffuse or porous patterns. How these mesoscale patterns arise remains largely unexplored. An agent-based flocking model was developed to investigate CAF properties and their involvement in emergent alignment. The model established that aligning cells had a requirement of highly persistent migration coupled with an active cell-cell collision guidance mechanism. The model predicted that alignment was a fragile state which could be easily destroyed in a heterogeneous population. These findings were confirmed experimentally. The model was then extended to include a second underlying layer of ECM fibres that the CAFs could produce, degrade and rearrange but were also instructed to follow, constituting a CAF-ECM feedback loop. This mechanism was capable of generating diverse matrix patterns, reminiscent of those seen in vivo. The model was challenged to unpick the process of interconversion between matrix patterns as seen in cancer, wound healing and ageing, which it elucidated with considerable success. Finally, clinical samples of ECM were quantified to establish if certain metrics of ECM architecture could be useful clinical prognostic factors. Early results suggest this to be true. Matrix patterns were quantified by a carefully constructed software pipeline suitable for use by other researchers on versatile data samples.Open Acces