School of Biomedical Engineering, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, China.
Department of Quantitative Health Sciences, Cleveland Clinic, Cleveland, OH, USA.
Eur Urol Focus. 2021 Jul;7(4):706-709. doi: 10.1016/j.euf.2021.07.006. Epub 2021 Aug 3.
A better understanding of the tumor immune microenvironment (TIME) could lead to accurate diagnosis, prognosis, and treatment stratification. Although molecular analyses at the tissue and/or single cell level could reveal the cellular status of the tumor microenvironment, these approaches lack information related to spatial-level cellular distribution, co-organization, and cell-cell interaction in the TIME. With the emergence of computational pathology coupled with machine learning (ML) and artificial intelligence (AI), ML- and AI-driven spatial TIME analyses of pathology images could revolutionize our understanding of the highly heterogeneous and complex molecular architecture of the TIME. In this review we highlight recent studies on spatial TIME analysis of pathology slides using state-of-the-art ML and AI algorithms. PATIENT SUMMARY: This mini-review reports recent advances in machine learning and artificial intelligence for spatial analysis of the tumor immune microenvironment in pathology slides. This information can help in understanding the spatial heterogeneity and organization of cells in patient tumors.
更好地了解肿瘤免疫微环境（TIME）可以实现准确的诊断、预后和治疗分层。尽管组织和/或单细胞水平的分子分析可以揭示肿瘤微环境的细胞状态，但这些方法缺乏与 TIME 中空间水平细胞分布、共组织和细胞-细胞相互作用相关的信息。随着计算病理学与机器学习（ML）和人工智能（AI）的结合，基于 ML 和 AI 的病理学图像时空分析可能会彻底改变我们对 TIME 高度异质和复杂的分子结构的理解。在这篇综述中，我们强调了最近使用最先进的 ML 和 AI 算法对病理学幻灯片进行时空 TIME 分析的研究。
这篇迷你综述报告了机器学习和人工智能在病理学幻灯片中进行肿瘤免疫微环境的空间分析方面的最新进展。这些信息可以帮助理解患者肿瘤中细胞的空间异质性和组织方式。
