ArticleJCO clinical cancer informatics2019
HistoQC: An Open-Source Quality Control Tool for Digital Pathology Slides.
Article in JCO clinical cancer informatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 181 papers, 1 of them a synthesis that pooled it.
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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
181 citing papers in PubMed, 1 synthesis or guideline pooled it, 347 citations in OpenAlex.
- Best Practice Recommendations for the Implementation of a Digital Pathology Workflow in the Anatomic Pathology Laboratory by the European Society of Digital and Integrative Pathology (ESDIP).Diagnostics (Basel, Switzerland) · 2021Guideline
- AI-Based Pathology Classifier Predicts Sensitivity to Enzalutamide in Metastatic Hormone-Sensitive Prostate Cancer: A Biomarker Analysis of the ENZAMET Trial.Clinical cancer research : an official journal of the American Association for Cancer Research · 2026Trial
- Computationally Derived Spatial Immune Signature Identifies Trastuzumab Responders in HER2+ Breast Cancer: NSABP B-41 Clinical Trial Validation.Clinical cancer research : an official journal of the American Association for Cancer Research · 2026Trial
- A guide for the deployment, validation, and accreditation of clinical digital pathology tools.Journal of pathology informatics · 2026Article
- Article
- Deep Learning-Assisted Quality Control of Histology Teaching Slides: Detection and Localization of Tissue Fold Artifacts in H&E-Stained Images.Bioengineering (Basel, Switzerland) · 2026Article
- Converting whole slide images from DICOM to ScanScope Virtual Slide-like TIFF: a practical workaround for teaching, research, and collaborative diagnosis.Virchows Archiv : an international journal of pathology · 2026Article
- GrandQC adaptation as an artificial intelligence tool for quantitative artifact detection in hematoxylin and eosin whole-slide images-Simulation of quality control biopsies day.Journal of pathology informatics · 2026Article
- Multimodal spatial omics: From data acquisition to computational integration.Patterns (New York, N.Y.) · 2026Review
- Development and Validation of a Multimodal-Multitask Deep Learning Approach for Estimating Late Distant Recurrence Risk in HR-Positive Early Breast Cancer.Cancer research communications · 2026Article
- Diffusion attention expert model for predicting and semi-automatic localizing STAS in lung cancer histopathological images.Nature communications · 2026Article
- DigitAb: Domain-Adaptive Cell Type Prediction Method from Light Microscopy Images.bioRxiv : the preprint server for biology · 2026Article
- Weak supervision of H&E slides reveals systems-level biology and functional states that govern therapeutic resistance.bioRxiv : the preprint server for biology · 2026Article
- An AI framework for automated quality control of paraffin block and slide consistency: a clinical evaluation and human-machine comparison study.Virchows Archiv : an international journal of pathology · 2026Article
- Deep learning-driven recognition of panoramic tumor microenvironment features in H&E sections and its application.Journal for immunotherapy of cancer · 2026Review
- Enhancing liver fibrosis measurement: Deep learning and uncertainty analysis across multi-center cohorts.Journal of pathology informatics · 2026Article
- Fast organ-of-origin classification for digital pathology quality control.Journal of pathology informatics · 2026Article
- The critical role of standards for AI in digital pathology: Digital Pathology Association Concept Paper.Journal of pathology informatics · 2026Article
- Development and Evaluation of a Deep Learning Model for Ovarian Cancer Histotype Classification Using Whole-Slide Imaging.Journal of imaging · 2026Article
- Computational pathology features of immune architecture predict clinically relevant outcomes in small-cell lung cancer (SCLC).NPJ precision oncology · 2026Article
121 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
5 authors at 4 institutions in 1 country.
Funding
Abstract
purposeDigital pathology (DP), referring to the digitization of tissue slides, is beginning to change the landscape of clinical diagnostic workflows and has engendered active research within the area of computational pathology. One of the challenges in DP is the presence of artefacts and batch effects, unintentionally introduced during both routine slide preparation (eg, staining, tissue folding) and digitization (eg, blurriness, variations in contrast and hue). Manual review of glass and digital slides is laborious, qualitative, and subject to intra- and inter-reader variability. Therefore, there is a critical need for a reproducible automated approach of precisely localizing artefacts to identify slides that need to be reproduced or regions that should be avoided during computational analysis.
methodsHere we present HistoQC, a tool for rapidly performing quality control to not only identify and delineate artefacts but also discover cohort-level outliers (eg, slides stained darker or lighter than others in the cohort). This open-source tool employs a combination of image metrics (eg, color histograms, brightness, contrast), features (eg, edge detectors), and supervised classifiers (eg, pen detection) to identify artefact-free regions on digitized slides. These regions and metrics are presented to the user via an interactive graphical user interface, facilitating artefact detection through real-time visualization and filtering. These same metrics afford users the opportunity to explicitly define acceptable tolerances for their workflows.
resultsThe output of HistoQC on 450 slides from The Cancer Genome Atlas was reviewed by two pathologists and found to be suitable for computational analysis more than 95% of the time.
conclusionThese results suggest that HistoQC could provide an automated, quantifiable, quality control process for identifying artefacts and measuring slide quality, in turn helping to improve both the repeatability and robustness of DP workflows.
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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.