ICF: NIRG: HistoMaps: Stain agnostic feature representations to identify clinically relevant traits in the tumour microenvironment

ICF：NIRG：HistoMaps：染色不可知的特征表示，以识别肿瘤微环境中的临床相关特征

• 批准号: MR/X011585/1
• 负责人: Shan Raza
• 金额: $ 51.05万
• 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: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=MR%2FX011585%2F1
• 关键词: ICF; NIRG; HistoMaps; Stain; agnostic

In early years of computational pathology, the algorithms were mainly focussed on segmentation and identification of objects such as nuclei, glands, ducts, vessels, and other patterns which are of interest to pathologists in every day clinical practice. The concept was to assist pathologists in identifying patterns which are difficult to eyeball over the huge landscape of cancer tissue in a whole slide image (WSI). The advent of modern CPath algorithms based on deep learning (DL) found that there are hidden features which humans usually ignore due to inattentional blindness. Therefore, CPath has moved beyond identification and classification of individual patterns within a whole slide image (WSI) towards WSI-level or case-level diagnosis, mutation and therapeutic response prediction discovering new morphological patterns, tissue phenotypes, even surpassing pathologist performance in some cases. On the other hand, DL algorithms are usually considered to be a black box due to lack of interpretability of the learnt features which makes it difficult to understand the biology of different diseases. One of the reasons is our inability to analyse huge landscapes in the tumour microenvironment (TME) where the WSIs are divided into small patches before analysis due to hardware limitations and complex DL architectures required for the analysis of images from different stains and modalities. The challenge is the gigapixel size of the WSIs containing the landscape of cancer which on one hand compels exploration but at the same time faces technological challenges. Due to tumour heterogeneity these small patches are usually not representative of the WSIs. Therefore, we need to develop techniques which can analyse WSIs without dividing them into smaller patches keeping the spatial information intact. This not only allows to overcome tumour heterogeneity limitations but helps in identifying heterogenous regions and embedded spatial relationships linked to patient outcome and other clinical variables. These techniques should be able to overcome the practical limitations of the hardware, invariant to the input stains and should be able to help with interpretability and biological understanding of the TME. The algorithmic limitations are currently being tackled by WSI-level weakly supervised labels or compressed representations. These approaches have some major drawbacks e.g., these approaches discard the essential spatial information required to incorporate cell-to-cell interactions in clinically significant regions during compression and are focussed mostly on identification or classification of disease into sub-categories where the DL model is treated as a black box. Analysis of TME at the cellular level is important to understand mechanisms in cancer where tumour heterogeneity plays a significant role. Multiplexed Immunofluorescence (MxIF) images provide additional data to subtype individual cells on the same tissue section which is not currently possible with existing brightfield approaches. There have been recent advances in whole slide image fluorescence imaging which allow scanning of WSIs with multiple markers. Therefore, we need stain and modality agnostic approaches which can analyse WSIs without losing spatial information at the cellular level so the rich data can be mined for better understanding of cancer. We propose to build on existing technology and utilise the extracted information to understand TME interactions at the whole slide image level. In this project, we will develop stain agnostic techniques to analyse and identify patterns in whole slide images (WSIs) by creating HistoMaps which can be directly related to biologically meaningful and clinically relevant parameters i.e., mutations, survival and response to therapy linking histology landscapes to clinical variables for better understanding of cancer helping oncologists to make informed decisions on therapeutic interventions and assisting pharma to develop new targets.

在计算病理学的早期，算法主要集中于对象的分割和识别，例如细胞核、腺体、导管、血管和病理学家在日常临床实践中感兴趣的其他模式。这个概念是为了帮助病理学家识别在整个幻灯片图像（WSI）中难以通过肉眼观察癌症组织的巨大景观的模式。基于深度学习（DL）的现代 CPath 算法的出现发现，存在一些隐藏的特征，而人类通常由于不注意的盲目性而忽略了这些特征。因此，CPath 已经超越了整个幻灯片图像 (WSI) 内个体模式的识别和分类，转向 WSI 级别或病例级别的诊断、突变和治疗反应预测，发现新的形态模式、组织表型，甚至在某些情况下超越了病理学家的表现。另一方面，由于学习特征缺乏可解释性，深度学习算法通常被认为是黑匣子，这使得理解不同疾病的生物学变得困难。原因之一是我们无法分析肿瘤微环境 (TME) 中的巨大景观，在分析之前，由于硬件限制和分析不同染色和模式的图像所需的复杂 DL 架构，WSI 被分成小块。挑战在于包含癌症景观的 WSI 大小为十亿像素，一方面需要探索，但同时也面临技术挑战。由于肿瘤异质性，这些小斑块通常不能代表 WSI。因此，我们需要开发能够分析 WSI 的技术，而无需将它们分成更小的块，从而保持空间信息完整。这不仅可以克服肿瘤异质性限制，还有助于识别异质区域以及与患者结果和其他临床变量相关的嵌入空间关系。这些技术应该能够克服硬件的实际限制，不受输入染色的影响，并且应该能够帮助 TME 的可解释性和生物学理解。目前，WSI 级别的弱监督标签或压缩表示正在解决算法限制。这些方法有一些主要缺点，例如，这些方法丢弃了在压缩过程中将细胞间相互作用纳入临床重要区域所需的基本空间信息，并且主要侧重于将疾病识别或分类为子类别，其中深度学习模型被视为黑匣子。细胞水平上的 TME 分析对于了解肿瘤异质性发挥重要作用的癌症机制非常重要。多重免疫荧光 (MxIF) 图像提供了额外的数据来对同一组织切片上的单个细胞进行亚型分类，这在现有的明场方法中目前是不可能的。整个载玻片图像荧光成像技术取得了最新进展，允许使用多个标记扫描 WSI。因此，我们需要与染色和模态无关的方法来分析 WSI，而不会丢失细胞水平的空间信息，以便挖掘丰富的数据以更好地了解癌症。我们建议以现有技术为基础，利用提取的信息来理解整个幻灯片图像级别的 TME 交互。在这个项目中，我们将开发染色不可知论技术，通过创建 HistoMaps 来分析和识别整个幻灯片图像 (WSI) 中的模式，该 HistoMaps 可以与具有生物学意义和临床相关参数直接相关，即突变、生存和对治疗的反应，将组织学景观与临床变量联系起来，以便更好地了解癌症，帮助肿瘤学家就治疗干预措施做出明智的决定，并协助制药公司开发新的治疗方法。 目标。