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

• URL: http://arxiv.org/abs/2512.19954v1
• Date: Tue, 23 Dec 2025 00:58:27 GMT
• Title: HistoWAS: A Pathomics Framework for Large-Scale Feature-Wide Association Studies of Tissue Topology and Patient Outcomes
• Authors: Yuechen Yang, Junlin Guo, Yanfan Zhu, Jialin Yue, Junchao Zhu, Yu Wang, Shilin Zhao, Haichun Yang, Xingyi Guo, Jovan Tanevski, Laura Barisoni, Avi Z. Rosenberg, Yuankai Huo,
• Abstract summary: HistoWAS (Histology-Wide Association Study) is a computational framework designed to link tissue spatial organization to clinical outcomes.<n>HistoWAS implements (1) a feature space that augments conventional metrics with 30 topological and spatial features; and (2) an association study engine, inspired by Phenome-Wide Association Studies (PheWAS)<n>As a proof of concept, we applied HistoWAS to analyze a total of 102 features using 385 PAS-stained WSIs from 206 participants in the Kidney Precision Medicine Project (KPMP)
• Score: 4.940115935118112
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• A Semantically Enhanced Generative Foundation Model Improves Pathological Image Synthesis [82.01597026329158]
  We introduce a Correlation-Regulated Alignment Framework for Tissue Synthesis (CRAFTS) for pathology-specific text-to-image synthesis.<n>CRAFTS incorporates a novel alignment mechanism that suppresses semantic drift to ensure biological accuracy.<n>This model generates diverse pathological images spanning 30 cancer types, with quality rigorously validated by objective metrics and pathologist evaluations.
  arXiv  Detail & Related papers  (2025-12-15T10:22:43Z)
• GRASPing Anatomy to Improve Pathology Segmentation [67.98147643529309]
  We introduce GRASP, a modular plug-and-play framework that enhances pathology segmentation models.<n>We evaluate GRASP on two PET/CT datasets, conduct systematic ablation studies, and investigate the framework's inner workings.
  arXiv  Detail & Related papers  (2025-08-05T12:26:36Z)
• PRING: Rethinking Protein-Protein Interaction Prediction from Pairs to Graphs [88.98041407783502]
  PRING is the first benchmark that evaluates protein-protein interaction prediction from a graph-level perspective.<n> PRING curates a high-quality, multi-species PPI network dataset comprising 21,484 proteins and 186,818 interactions.
  arXiv  Detail & Related papers  (2025-07-07T15:21:05Z)
• Segment Anything in Pathology Images with Natural Language [10.525414795571393]
  PathSegmentor is the first text-prompted segmentation foundation model designed specifically for pathology images.<n>We also introduce PathSeg, the largest and most comprehensive dataset for pathology segmentation.
  arXiv  Detail & Related papers  (2025-06-26T04:01:40Z)
• AI-powered virtual tissues from spatial proteomics for clinical diagnostics and biomedical discovery [4.608421774700912]
  We present Virtual Tissues (VirTues), a foundation model framework for biological tissues that operates across the molecular, cellular and tissue scale.<n>VirTues introduces innovations in transformer architecture design, including a novel tokenization scheme that captures both spatial and marker dimensions.<n>As a generalist model, VirTues outperforms existing approaches across clinical diagnostics, biological discovery and patient case retrieval tasks.
  arXiv  Detail & Related papers  (2025-01-10T15:17:27Z)
• Querying functional and structural niches on spatial transcriptomics data [7.114887702723693]
  spatial transcriptomics enables gene expression profiling in spatial contexts.<n>It has been revealed that spatial niches serve as cohesive and recurrent units in physiological and pathological processes.<n>We defined the Niche Query Task, which is to identify similar niches across ST samples given a niche of interest (NOI)<n>We developed QueST, a specialized method for solving this task.
  arXiv  Detail & Related papers  (2024-10-14T16:01:27Z)
• Anatomy-guided Pathology Segmentation [56.883822515800205]
  We develop a generalist segmentation model that combines anatomical and pathological information, aiming to enhance the segmentation accuracy of pathological features. Our Anatomy-Pathology Exchange (APEx) training utilizes a query-based segmentation transformer which decodes a joint feature space into query-representations for human anatomy. In doing so, we are able to report the best results across the board on FDG-PET-CT and Chest X-Ray pathology segmentation tasks with a margin of up to 3.3% as compared to strong baseline methods.
  arXiv  Detail & Related papers  (2024-07-08T11:44:15Z)
• GenBench: A Benchmarking Suite for Systematic Evaluation of Genomic Foundation Models [56.63218531256961]
  We introduce GenBench, a benchmarking suite specifically tailored for evaluating the efficacy of Genomic Foundation Models. GenBench offers a modular and expandable framework that encapsulates a variety of state-of-the-art methodologies. We provide a nuanced analysis of the interplay between model architecture and dataset characteristics on task-specific performance.
  arXiv  Detail & Related papers  (2024-06-01T08:01:05Z)
• SpecstatOR: Speckle statistics-based iOCT Segmentation Network for Ophthalmic Surgery [39.66047935237083]
  We use statistical analysis of speckle patterns to incorporate statistical pathology-specific prior knowledge. Our findings indicate statistically different speckle patterns within the retina and between retinal layers and surgical tools. The proposed segmentation model aims to refine the statistical findings based on prior tissue understanding.
  arXiv  Detail & Related papers  (2024-04-30T11:49:29Z)
• GeoTop: Advancing Image Classification with Geometric-Topological Analysis [0.0]
  Topological Data Analysis and Lipschitz-Killing Curvatures are used as powerful tools for feature extraction and classification. We investigate the potential of combining both methods to improve classification accuracy. This approach has the potential to advance our understanding of complex biological processes in various biomedical applications.
  arXiv  Detail & Related papers  (2023-11-08T23:38:32Z)
• K-Space-Aware Cross-Modality Score for Synthesized Neuroimage Quality Assessment [71.27193056354741]
  The problem of how to assess cross-modality medical image synthesis has been largely unexplored. We propose a new metric K-CROSS to spur progress on this challenging problem. K-CROSS uses a pre-trained multi-modality segmentation network to predict the lesion location.
  arXiv  Detail & Related papers  (2023-07-10T01:26:48Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.