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

MICA：开发组织学景观的微社区分析 (MiCAHiL)

• 批准号: MR/P015476/1
• 负责人: Nasir Rajpoot
• 金额: $ 77.2万
• 依托单位: University of Warwick
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2017
• 资助国家: 英国
• 起止时间: 2017 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=MR%2FP015476%2F1
• 关键词: MICA; Developing; Micro; Community; Analytics

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.

目前用于诊断和分级许多疾病（包括大多数实体瘤）的“金标准”主要基于专家组织病理学家对粘在载玻片上的可疑组织标本的极薄（仅几微米厚）切片的视觉显微镜评估。几十年来，这种做法或多或少保持不变，并导致主观和可变的诊断。然而，最近在英国的一些诊断病理学实验室采用数字载玻片扫描仪，标志着NHS信托基金病理学实践的新革命，我们当地的NHS信托基金是该国第一个使用数字扫描图像的组织切片进行常规诊断的机构。数字切片扫描仪为每张组织学切片生成一张数十亿像素的全切片图像（WSI），每张图像包含成千上万种不同类型细胞及其空间关系的丰富信息。该项目旨在引入一种新的组织微环境分析和计算机分析范式。我们将开发用于图像分析的复杂工具，以揭示与疾病亚组（例如，癌症可能更积极地进展的患者群体）相关的空间趋势和模式，并在我们当地的NHS信托机构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的发展而成为可能，例如那些允许我们识别WSI中不同种类的单个细胞的发展，从而使我们能够描绘出我们称之为“组织学模型”的组织微环境的彩色图像。了解和分析组织微环境不仅对于评估疾病的级别和侵袭性以及预测其病程至关重要，而且还可以帮助我们更好地了解基因组变异如何表现为组织微环境中的结构变化。我们将开发工具和技术，以提取在复杂的组织学景观中发现的不同细胞或细胞集落的空间结构和“社会”相互作用中发现的模式和趋势。我们的目标是建立图像分析的有效使用，以定量和系统的方式了解组织学景观，促进发现基于图像的疾病进展和生存标志物，这些标志物直观，具有生物学意义，和临床相关性-最终导致治疗方案的最佳选择该项目将分析来自结直肠癌患者队列的真实的图像数据和相关的临床和基因组学数据作为案例研究。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。通过与我们的工业合作伙伴英特尔合作，我们将在临床环境中测试和展示我们的方法的有效性，这可能会为患者提供更好的医疗保健服务，并为NHS节省潜在的成本。

项目成果

Tumour Nuclear Morphometrics Predict Survival in Lung Adenocarcinoma

• DOI: 10.1109/access.2021.3049582
• 发表时间: 2021-01-01
• 期刊: IEEE ACCESS
• 影响因子: 3.9
• 作者: Alsubaie, Najah M.;Snead, David;Rajpoot, Nasir M.
• 通讯作者: Rajpoot, Nasir M.

Development and validation of a weakly supervised deep learning framework to predict the status of molecular pathways and key mutations in colorectal cancer from routine histology images: a retrospective study.

• DOI: 10.1016/s2589-7500(21)00180-1
• 发表时间: 2021-12
• 期刊: The Lancet. Digital health
• 作者: Bilal M;Raza SEA;Azam A;Graham S;Ilyas M;Cree IA;Snead D;Minhas F;Rajpoot NM
• 通讯作者: Rajpoot NM

Diagnostic concordance and discordance in digital pathology: a systematic review and meta-analysis.

• DOI: 10.1136/jclinpath-2020-206764
• 发表时间: 2021-07
• 期刊: Journal of clinical pathology
• 影响因子: 3.4
• 作者: Azam AS;Miligy IM;Kimani PK;Maqbool H;Hewitt K;Rajpoot NM;Snead DRJ
• 通讯作者: Snead DRJ

Consistency Regularisation in Varying Contexts and Feature Perturbations for Semi-Supervised Semantic Segmentation of Histology Images

• DOI: 10.48550/arxiv.2301.13141
• 发表时间: 2023-01
• 期刊: Medical image analysis
• 影响因子: 10.9
• 作者: R. M. S. Bashir;Talha Qaiser;S. Raza;N. Rajpoot

SynCLay: Interactive synthesis of histology images from bespoke cellular layouts.

SynCLay：根据定制的细胞布局交互式合成组织学图像。

• DOI: 10.1016/j.media.2023.102995
• 发表时间: 2023
• 期刊: Medical image analysis
• 影响因子: 10.9
• 作者: Deshpande S