MICA: Developing Micro-Community Analytics for Histology Landscapes (MiCAHiL)

目前对许多疾病(包括大多数实体肿瘤)的诊断和分级的黄金标准主要是基于专家组织病理学家对粘贴在玻璃片上的可疑组织标本的极薄(只有几微米厚)切片的视觉显微镜评估。这种做法几十年来基本保持不变，并导致了主观和可变的诊断。然而，英国一些诊断病理学实验室最近采用了数字玻片扫描仪，这标志着NHS信托基金在病理学实践中发生了一场新的革命，我们当地的NHS信托基金是全国第一家使用数字扫描的组织切片图像进行常规诊断的机构。数字载玻片扫描仪为每个组织学载玻片生成一个几十亿像素的全载玻片图像(WSI)，每个图像包含有关数万种不同类型的细胞及其相互之间的空间关系的丰富信息。该项目旨在为组织微环境的分析和计算机化轮廓引入一种新的范例。我们将开发复杂的图像分析工具，以揭示与疾病亚组(例如，癌症进展可能更具侵袭性的患者组)相关的空间趋势和模式，并在我们当地的NHS信托基金中部署这些工具进行临床验证。这将通过进一步推进我们小组最近取得的进展而成为可能，例如那些使我们能够识别信息社会世界首脑会议中不同种类的单个细胞的进展，从而使我们能够描绘一幅我们称之为“组织学景观”的组织微环境的彩色图景。了解和分析组织微环境不仅对评估疾病的级别和侵袭性以及预测其进程至关重要，而且还可以帮助我们更好地理解基因组变化是如何表现为组织微环境中的结构性变化的。我们将开发工具和技术，以提取在空间结构中发现的模式和趋势，以及在复杂的组织学景观中发现的不同细胞或细胞群体的“社会”相互作用。我们的目标是建立对图像分析的有效利用，以定量和系统的方式了解组织学图景，促进基于图像的疾病进展和生存的直观、生物学意义和临床相关的标记的发现-最终导致针对个别患者定制的治疗方案的最佳选择(S)。该项目将分析来自结直肠癌患者队列的真实图像数据以及相关的临床和基因组数据作为案例研究。该项目的研究人员将与临床合作者密切合作，以确保数据中发现的空间趋势和模式的生物学意义和临床相关性。我们将与我们的工业合作伙伴英特尔合作，在临床环境中测试和展示我们方法的有效性，这可能会为患者提供更好的医疗保健，并可能为NHS节省成本。

The current 'gold standard' for diagnosis and grading of many diseases (including most solid tumours) is largely based on an expert histopathologist's visual microscopic assessment of an extremely thin (only a few micrometers thick) section of the suspicious tissue specimen glued to a glass slide. This practice has remained more or less the same for several decades, and results in subjective and variable diagnosis. However, the recent uptake of digital slide scanners by some diagnostic pathology laboratories in the UK marks a new revolution in pathology practice in the NHS trusts, with our local NHS trust being the first one in the country to use digitally scanned images of tissue slides for routine diagnostics. The digital slide scanner produces a multi-gigapixel whole-slide image (WSI) for each histology slide, with each image containing rich information about tens of thousands of different kinds of cells and their spatial relationships with each other.This project aims to introduce a novel paradigm for analytics and computerised profiling of tissue microenvironment. We will develop sophisticated tools for image analytics in order to reveal spatial trends and patterns associated with disease sub-groups (for example, patient groups whose cancer is likely to advance more aggressively) and deploy those tools for clinical validation at our local NHS trust. This will be made possible by further advancing recent developments made in our group, such as those allowing us to recognise individual cells of different kinds in the WSIs consequently enabling us to paint a colourful picture of the tissue microenvironment which we term as the 'histology landscape'. Understanding and analysing the tissue microenvironment is not only crucial to assessing the grade and aggressiveness of disease and for predicting its course, it can also help us better understand how genomic alterations manifest themselves as structural changes in the tissue microenvironment. We will develop tools and techniques to extract patterns and trends found in the spatial structure and the 'social' interplay of different cells or colonies of cells found in the complex histology landscapes. Our goal is to establish the effective use of image analytics for understanding the histology landscape in a quantitative and systematic manner, facilitating the discovery of image-based markers of disease progression and survival that are intuitive, biologically meaningful, and clinically relevant - eventually leading to optimal selection of treatment option(s) customised to individual patients.This project will analyse real image data and associated clinical and genomics data from patient cohorts for colorectal cancer as a case study. The research staff on this project will work closely with clinical collaborators to ensure the biological significance and clinical relevance of spatial trends and patterns found in the data. In collaboration with our industrial partner Intel, we will test and demonstrate the effectiveness of our methods in a clinical setting potentially leading to better healthcare provision for patients and potential cost savings for the NHS.