通过空间特征分析，将组织结构与患者预后关联起来。
HistoWAS: A Pathomics Framework for Large-Scale Feature-Wide Association Studies of Tissue Topology and Patient Outcomes
- 引入30个基于地理信息系统的新空间特征，量化组织微结构
- 在385张肾组织切片上发现多个空间特征与临床结果相关
- 适合病理学、生物信息学研究者探索组织拓扑与疾病关系
全切片图像（WSIs）的高通量病理组学分析为研究组织特征和生物标志物发现提供了新机遇。然而，组织在微观与宏观环境中的特征与临床的相关性受限于缺乏工具来测量个体结构特征的空间交互及其与临床参数的关联。为此，我们提出HistoWAS（组织广义关联研究），一种将组织空间组织与临床结局关联的计算框架。具体包括：（1）一个特征空间，将传统指标扩展为30个源自地理信息系统（GIS）点模式分析的拓扑与空间特征，用于量化组织微架构；（2）一个关联研究引擎，受表型广义关联研究（PheWAS）启发，对每个特征进行大规模单变量回归并做统计校正。作为概念验证，我们在肾精准医学计划（KPMP）的206名参与者共385张PAS染色的全切片图像上，分析了102个特征（72个传统对象级特征与30个新空间特征）。代码与数据已公开于https://github.com/hrlblab/histoWAS。
原文摘要 · Abstract (English)
High-throughput "pathomic" analysis of Whole Slide Images (WSIs) offers new opportunities to study tissue characteristics and for biomarker discovery. However, the clinical relevance of the tissue characteristics at the micro- and macro-environment level is limited by the lack of tools that facilitate the measurement of the spatial interaction of individual structure characteristics and their association with clinical parameters. To address these challenges, we introduce HistoWAS (Histology-Wide Association Study), a computational framework designed to link tissue spatial organization to clinical outcomes. Specifically, HistoWAS implements (1) a feature space that augments conventional metrics with 30 topological and spatial features, adapted from Geographic Information Systems (GIS) point pattern analysis, to quantify tissue micro-architecture; and (2) an association study engine, inspired by Phenome-Wide Association Studies (PheWAS), that performs mass univariate regression for each feature with statistical correction. As a proof of concept, we applied HistoWAS to analyze a total of 102 features (72 conventional object-level features and our 30 spatial features) using 385 PAS-stained WSIs from 206 participants in the Kidney Precision Medicine Project (KPMP). The code and data have been released to https://github.com/hrlblab/histoWAS.
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