病理学中的多重成像分析：分析方法与数字工具包的全面综述
Multiplex Imaging Analysis in Pathology: a Comprehensive Review on Analytical Approaches and Digital Toolkits
AI总结：
本综述梳理病理学多重成像领域的发展现状，介绍相关分析工作流与PathML等AI驱动的计算工具，阐明其在处理海量多维成像数据、支撑疾病研究与诊断中的关键作用。
摘要：
传统组织病理学长期以来一直是疾病诊断的核心手段，依赖于对组织切片的目视检查。免疫组织化学技术有助于检测特定生物标志物，但受限于其单标志物检测方式，无法完整捕捉组织微环境的全貌。多重成像技术的出现，例如多重免疫荧光技术和空间转录组学技术，能够在同一张切片上同时可视化多种生物标志物，为形态学数据补充了分子和空间层面的信息。这为研究组织微环境、细胞相互作用以及疾病机制提供了更全面的视角，对于理解疾病进展、预后和治疗反应至关重要。然而，多重成像产生的海量数据需要复杂的计算方法来完成预处理、分割、特征提取和空间分析等任务。这些工具对于处理大规模多维数据集、将原始成像数据转化为可落地的临床见解至关重要。通过自动化劳动密集型任务并提升可重复性和准确性，计算工具在诊断和研究领域发挥着核心作用。本综述梳理了病理学领域多重成像的当前发展格局，详细介绍了工作流程以及PathML等关键技术——PathML是一个由AI驱动的平台，可简化图像分析流程，让临床和研究场景下的复杂数据集解读变得更易实现。
英文摘要：
Conventional histopathology has long been essential for disease diagnosis, relying on visual inspection of tissue sections. Immunohistochemistry aids in detecting specific biomarkers but is limited by its single-marker approach, restricting its ability to capture the full tissue environment. The advent of multiplexed imaging technologies, like multiplexed immunofluorescence and spatial transcriptomics, allows for simultaneous visualization of multiple biomarkers in a single section, enhancing morphological data with molecular and spatial information. This provides a more comprehensive view of the tissue microenvironment, cellular interactions, and disease mechanisms - crucial for understanding disease progression, prognosis, and treatment response. However, the extensive data from multiplexed imaging necessitates sophisticated computational methods for preprocessing, segmentation, feature extraction, and spatial analysis. These tools are vital for managing large, multidimensional datasets, converting raw imaging data into actionable insights. By automating labor-intensive tasks and enhancing reproducibility and accuracy, computational tools are pivotal in diagnostics and research. This review explores the current landscape of multiplexed imaging in pathology, detailing workflows and key technologies like PathML, an AI-powered platform that streamlines image analysis, making complex dataset interpretation accessible for clinical and research settings.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
