ArticleJournal of biomedical informatics2017
Developing the Quantitative Histopathology Image Ontology (QHIO): A case study using the hot spot detection problem.
Article in Journal of biomedical informatics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Ontologies for Liver Diseases Representation: A Systematic Literature Review.Journal of digital imaging · 2020Pooled it
- An open-source platform for structured annotation and computational workflows in digital pathology research.Scientific reports · 2025Article
- Modelling digital health data: The ExaMode ontology for computational pathology.Journal of pathology informatics · 2023Article
- Ontologies and Knowledge Graphs in Oncology Research.Cancers · 2022Review
- COVID-19 Biomarkers in research: Extension of the OncoMX cancer biomarker data model to capture biomarker data from other diseases.bioRxiv : the preprint server for biology · 2020Article
- Deep learning-based image analysis methods for brightfield-acquired multiplex immunohistochemistry images.Diagnostic pathology · 2020Article
- Artificial intelligence driven next-generation renal histomorphometry.Current opinion in nephrology and hypertension · 2020Review
- Annotations, Ontologies, and Whole Slide Images - Development of an Annotated Ontology-Driven Whole Slide Image Library of Normal and Abnormal Human Tissue.Journal of pathology informatics · 2019Article
- An Image Analysis Resource for Cancer Research: PIIP-Pathology Image Informatics Platform for Visualization, Analysis, and Management.Cancer research · 2017Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Interoperability across data sets is a key challenge for quantitative histopathological imaging. There is a need for an ontology that can support effective merging of pathological image data with associated clinical and demographic data. To foster organized, cross-disciplinary, information-driven collaborations in the pathological imaging field, we propose to develop an ontology to represent imaging data and methods used in pathological imaging and analysis, and call it Quantitative Histopathological Imaging Ontology - QHIO. We apply QHIO to breast cancer hot-spot detection with the goal of enhancing reliability of detection by promoting the sharing of data between image analysts.
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Registered trials
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