HistoWAS: A Pathomics Framework for Large-Scale Feature-Wide Association Studies of Tissue Topology and Patient Outcomes

2025-12-22

AI Summary

Current methods lack tools to quantify spatial interplay between microscopic and macroscopic tissue architectures and their clinical associations, hindering the clinical translation of whole-slide image (WSI)-based histopathomics. To address this, we propose HistoWAS—the first histology-wide association study (HWAS) paradigm for WSIs—integrating geographic information system (GIS)-inspired point pattern analysis to extract 30-dimensional spatial topological features (e.g., lymphocyte clustering entropy, tubular alignment heterogeneity) alongside 72 conventional morphometric features. We further develop a high-throughput spatial feature–clinical outcome association engine leveraging phenome-wide association study (PheWAS)-style modeling with rigorous multiple-testing correction (FDR/Bonferroni). Validated on 385 PAS-stained renal WSI slides, HistoWAS identifies multiple statistically significant and biologically interpretable spatial biomarkers. All code and data are fully open-sourced.

Technology Category

• Data Mining & Knowledge Management: Mining of Spatial, Temporal or Spatio-Temporal Data
• Computer Vision: Other Foundations of Computer Vision
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding

Abstract

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.

Problem

• Links tissue spatial organization to clinical outcomes
• Quantifies tissue micro-architecture using topological features
• Performs mass univariate regression for feature-wide association studies

Innovation

• Augments conventional metrics with 30 topological spatial features
• Implements mass univariate regression with statistical correction
• Links tissue spatial organization to clinical outcomes computationally