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

前列腺是男性最常见的癌症部位，全世界每年诊断出120万新病例。主要诊断途径需要MRI，然后进行活检以获得针芯组织。样本的组织学分析用于基于长期建立的Gleason评分来诊断癌症的阶段，该评分对组织的形态变化进行分类，较高的评分与更具侵袭性的疾病和更差的预后相关。格里森量表于20世纪60年代建立，并在2005年和2014年由国际专家会议更新。改进集中在特定类型的形成，如筛状腺，肾小球样腺，粘液癌，与侵袭性疾病。然而，Gleason评分仍然是病理学家主导的诊断模式，基于经验。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辅助活检中的前列腺癌诊断的能力。然而，标准化、再现性和来源仍然没有得到充分解决。此外，组学数据和空间生物标志物读数尚未整合到AI辅助诊断过程中。我们建议开发AI和机器学习算法，将多个表型和组学数据流整合到一个新的计算机框架中，该框架可以表征前列腺活检组织的癌症阶段和进展。组织表型分析将包括苏木精和伊红（H&E）染色的临床标准，沿着新的组织涂料，如DRAQ 5，其选择性地结合细胞核，这是癌症分层的基本特征。此外，空间转录组学Nanostring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组学数据也将用于制定一种新的多维空间方法，适用于人工智能和机器学习工具的开发和测试。我们将使用商业上可用的软件包，如Tensorflow和Python中的Keras，U-Net卷积网络，以及内部开发的系统来促进新的多物理数据集成。这项工作的发现元素将得到肿瘤微环境的基本表型的支持，这是我们研究小组在过去二十年中的主要焦点。

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