成像技术的进步彻底改变了我们深入分析病理组织结构的能力，生成具有无与伦比的空间分辨率的大量成像数据。这种类型的数据收集，即空间蛋白质组学，为各种人类疾病提供了宝贵的见解。同时，计算算法已经发展到可以管理这一进步中固有的空间蛋白质组学维度的增加。人们已经提出了许多基于成像的计算框架，例如计算病理学，用于研究和临床应用。然而，这些领域的发展需要不同的领域专业知识，这给它们的整合和进一步应用造成了障碍。本次审查旨在通过提出全面的指南来弥合这一分歧。我们整合了流行的计算方法，并概述了从图像处理到数据驱动、基于统计的生物标志物发现的路线图。此外，随着该领域走向与其他定量领域的接口，我们探索了未来的前景，为免疫肿瘤学的精准护理带来了重大希望。© 作者 2024。由牛津大学出版社出版。

Advancements in imaging technologies have revolutionized our ability to deeply profile pathological tissue architectures, generating large volumes of imaging data with unparalleled spatial resolution. This type of data collection, namely, spatial proteomics, offers invaluable insights into various human diseases. Simultaneously, computational algorithms have evolved to manage the increasing dimensionality of spatial proteomics inherent in this progress. Numerous imaging-based computational frameworks, such as computational pathology, have been proposed for research and clinical applications. However, the development of these fields demands diverse domain expertise, creating barriers to their integration and further application. This review seeks to bridge this divide by presenting a comprehensive guideline. We consolidate prevailing computational methods and outline a roadmap from image processing to data-driven, statistics-informed biomarker discovery. Additionally, we explore future perspectives as the field moves toward interfacing with other quantitative domains, holding significant promise for precision care in immuno-oncology.© The Author(s) 2024. Published by Oxford University Press.