ArticleCancer research2017
A Containerized Software System for Generation, Management, and Exploration of Features from Whole Slide Tissue Images.
Article in Cancer research, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
32 citing papers in PubMed.
- Advancing open-source visual analytics in digital pathology: A systematic review of tools, trends, and clinical applications.Journal of pathology informatics · 2025Review
- An update on applications of digital pathology: primary diagnosis; telepathology, education and research.Diagnostic pathology · 2025Review
- Keratin 17 modulates the immune topography of pancreatic cancer.Journal of translational medicine · 2024Article
- Training pathologists to assess stromal tumour-infiltrating lymphocytes in breast cancer synergises efforts in clinical care and scientific research.Histopathology · 2024Review
- Reproducible Reporting of the Collection and Evaluation of Annotations for Artificial Intelligence Models.Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc · 2024Article
- Artificial Intelligence Applications in Lymphoma Diagnosis and Management: Opportunities, Challenges, and Future Directions.Journal of multidisciplinary healthcare · 2024Review
- Digital pathology and multimodal learning on oncology data.BJR artificial intelligence · 2024Review
- Initial interactions with the FDA on developing a validation dataset as a medical device development tool.The Journal of pathology · 2023Article
- The Importance of Quality Assurance in Radiation Oncology Clinical Trials.Seminars in radiation oncology · 2023Review
- Computational immunohistochemical mapping adds immune context to histological phenotypes in mouse models of colitis.Scientific reports · 2023Article
- Interoperable slide microscopy viewer and annotation tool for imaging data science and computational pathology.Nature communications · 2023Article
- Histology Image Viewer and Converter (HIVC): A High-Speed Freeware Software to View and Convert Whole Slide Histology Images.Computer methods in biomechanics and biomedical engineering. Imaging & visualization · 2023Article
- Pilot study to evaluate tools to collect pathologist annotations for validating machine learning algorithms.Journal of medical imaging (Bellingham, Wash.) · 2022Article
- General Roadmap and Core Steps for the Development of AI Tools in Digital Pathology.Diagnostics (Basel, Switzerland) · 2022Article
- Article
- Multimodal analysis suggests differential immuno-metabolic crosstalk in lung squamous cell carcinoma and adenocarcinoma.NPJ precision oncology · 2022Article
- An Expandable Informatics Framework for Enhancing Central Cancer Registries with Digital Pathology Specimens, Computational Imaging Tools, and Advanced Mining Capabilities.Journal of pathology informatics · 2022Article
- Radiation Oncology: Future Vision for Quality Assurance and Data Management in Clinical Trials and Translational Science.Frontiers in oncology · 2022Review
- Deep Learning-Based Mapping of Tumor Infiltrating Lymphocytes in Whole Slide Images of 23 Types of Cancer.Frontiers in oncology · 2021Article
- A Pathologist-Annotated Dataset for Validating Artificial Intelligence: A Project Description and Pilot Study.Journal of pathology informatics · 2021Article
Corrections and comments
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Authors and funding
12 authors.
Funding
Abstract
Well-curated sets of pathology image features will be critical to clinical studies that aim to evaluate and predict treatment responses. Researchers require information synthesized across multiple biological scales, from the patient to the molecular scale, to more effectively study cancer. This article describes a suite of services and web applications that allow users to select regions of interest in whole slide tissue images, run a segmentation pipeline on the selected regions to extract nuclei and compute shape, size, intensity, and texture features, store and index images and analysis results, and visualize and explore images and computed features. All the services are deployed as containers and the user-facing interfaces as web-based applications. The set of containers and web applications presented in this article is used in cancer research studies of morphologic characteristics of tumor tissues. The software is free and open source.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.