Artificial Intelligence assisted mapping of prostate cancer progression in patient biopsies with novel tissue labelling biomarkers - beyond Gleason Sc

人工智能利用新型组织标记生物标志物辅助绘制患者活检中前列腺癌进展图 - 超越格里森科学

• 批准号: 2887602
• 金额: --
• 依托单位: CARDIFF UNIVERSITY
• 依托单位国家: 英国
• 项目类别: Studentship
• 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=studentship-2887602
• 关键词: Artificial; Intelligence; assisted; mapping; prostate

The prostate gland is the site of the most common cancer in men, with 1.2 million new cases diagnosed annually worldwide. The main diagnostic pathway requires MRI followed by biopsy to obtain needle core tissue. Histopathology analysis of the samples is used to diagnose the stage of cancer based on the long-established Gleason score that classifies the morphological changes of the tissue, with higher scores associated with more aggressive disease and worse prognosis. The Gleason scale was established in the 1960's and updated in 2005 and 2014 by international conferences of experts. Refinements were centred around specific types of formation, such as cribriform glands, glomeruloid glands, and mucinous carcinoma, associated with aggressive disease. Nevertheless, Gleason scoring remains a pathologist-led diagnostic modality, based on experience. Over the past decade, artificial intelligent tools have demonstrated the ability to assist prostate cancer diagnosis in biopsies by employing image processing and pattern recognition technology. Standardisation, reproducibility, and provenance, however, are still not adequately addressed. Moreover, omic data and spatial biomarker readouts have not been integrated into the AI-assisted diagnostic process. We propose to develop AI and Machine Learning algorithms to integrate multiple streams of phenotypic and omic data into a novel in silico framework that characterises the prostate biopsy tissue for cancer stage and progression. Tissue phenotyping will include the clinical standard of hematoxylin and eosin (H&E) staining along with novel tissue paints, such as DRAQ5, which selectively binds to nuclei an essential feature of cancer stratification. Additionally, spatial transcriptomics Nanostring technology data will be used to identify features of the stroma. Tissue matching omic data from the European Stem Cell Institute will also be used to formulate a novel multi-dimensional space approach, suitable for AI and machine learning tools to be developed and tested. We will employ both commercially available packages, such as Tensorflow, and Keras in Python, U-Net convolutional networks, as well as in-house developed systems to facilitate novel multi-physics data integration. The discovery element of the work will be supported by the fundamental phenotyping of the tumour microenvironment that has been the main focus of our research group over the past two decades

前列腺是男性最常见癌症的所在地，每年在全球范围内诊断出120万例新病例。主要的诊断途径需要MRI，然后进行活检才能获得针核组织。对样品的组织病理学分析用于根据长期以来的格里森评分来诊断癌症的阶段，该评分对组织的形态变化进行了分类，而得分较高，与更具侵略性的疾病和较差的预后相关。格里森量表于1960年代建立，并于2005年和2014年由国际专家会议更新。改进以特定类型的形成为中心，例如与侵袭性疾病相关的特定类型的形成。然而，根据经验，格里森得分仍然是病理学家主导的诊断方式。在过去的十年中，人工智能工具已经证明了通过采用图像处理和模式识别技术来帮助前列腺癌诊断的能力。但是，仍然没有充分解决标准化，可重复性和出处。此外，没有将OMIC数据和空间生物标志物读数集成到AI辅助诊断过程中。我们建议开发AI和机器学习算法，以将表型和OMIC数据的多个流中的多个流动到硅框架中的小说中，该小说以前列腺活检组织的癌症阶段和进展为特征。组织表型将包括苏木精和曙红（H＆E）的临床标准以及新型的组织油漆，例如DRAQ5，它们选择性地与核结合了癌症分层的基本特征。此外，空间转录组学纳米串技术数据将用于识别基质的特征。来自欧洲干细胞研究所的组织匹配的组织匹配也将用于制定一种新型的多维空间方法，适用于正在开发和测试的AI和机器学习工具。我们将在Python，U-NET卷积网络以及内部开发的系统中使用商业上可用的软件包，例如Tensorflow和Keras，以促进新型的多物理数据集成。肿瘤微环境的基本表型将支持这项工作的发现元素，这是过去二十年来我们研究小组的主要重点