ReviewComputational and structural biotechnology journal2021
QuPath: The global impact of an open source digital pathology system.
Review in Computational and structural biotechnology journal, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 66 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
66 citing papers in PubMed, 110 citations in OpenAlex.
- DeepPathway: predicting pathway expression from histopathology images.Bioinformatics (Oxford, England) · 2026Article
- 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
- Challenges and opportunities of implementing open-source digital pathology in sub-Saharan Africa: Lessons learned from deploying QuPath for breast cancer biomarker assessment in Cameroon.Journal of pathology informatics · 2026Article
- Microstructural mapping of time-dependent diffusion MRI for predicting lymphovascular invasion in rectal cancer: a proof-of-concept investigation.Insights into imaging · 2026Article
- Mitochondrial Function Under Combined Oral Contraceptive Exposure in a Neuroblastoma Model: A Preliminary Investigation.Biomedicines · 2026Article
- Molecular classification and construction of prognostic risk model via machine learning based on metabolic-related genes in hepatocellular carcinoma.Translational cancer research · 2026Article
- Red Blood Cells Internalize Extracellular DNA via Apoptotic Bodies with Clinical Relevance to Cancer Patients.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Study on the expression level of folate receptor γ in thyroid cancer and its association with clinicopathological characteristics.Oncology letters · 2026Article
- ColonyQuant: automated quantification and morphometric analysis of pluripotent stem cell colonies.BMC bioinformatics · 2026Article
- Supervised machine learning to quantitatively assess the prognostic value of tumor-infiltrating lymphocytes in gastric cancer.BMC cancer · 2026Article
- Epithelial remodeling and microbial dysbiosis in the lower respiratory tract of vitamin A-deficient mouse lungs.Scientific reports · 2026Article
- Stromal Laminin Subunit Alpha 3 is a Novel Prognostic Marker for Breast Cancer.Clinical Medicine Insights. Oncology · 2026Article
- Automated Image-Based Profiling of Pluripotent Stem Cell Colonies.bioRxiv : the preprint server for biology · 2025Article
- Proof-of-Concept Study: Hyperspectral Imaging for Quantification of DKK-3 Expression in Oropharyngeal Carcinoma.Bioengineering (Basel, Switzerland) · 2025Article
- Article
- Article
- Interobserver Agreement in Immunohistochemical Evaluation of Folate Receptor Alpha (FRα) in Ovarian Cancer: A Multicentre Study.International journal of molecular sciences · 2025Article
- Digital pathology in tau research: A comparison of QuPath and HALO.Journal of neuropathology and experimental neurology · 2025Article
- CYP1A2 expression and activity in a large panel of human nonneoplastic surgical liver samples: Influence of genetics, lifestyle, and other factors.Drug metabolism and disposition: the biological fate of chemicals · 2025Article
- Advancing open-source visual analytics in digital pathology: A systematic review of tools, trends, and clinical applications.Journal of pathology informatics · 2025Review
6 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
QuPath, originally created at the Centre for Cancer Research & Cell Biology at Queen's University Belfast as part of a research programme in digital pathology (DP) funded by Invest Northern Ireland and Cancer Research UK, is arguably the most wildly used image analysis software program in the world. On the back of the explosion of DP and a need to comprehensively visualise and analyse whole slides images (WSI), QuPath was developed to address the many needs associated with tissue based image analysis; these were several fold and, predominantly, translational in nature: from the requirement to visualise images containing billions of pixels from files several GBs in size, to the demand for high-throughput reproducible analysis, which the paradigm of routine visual pathological assessment continues to struggle to deliver. Resultantly, large-scale biomarker quantification must increasingly be augmented with DP. Here we highlight the impact of the open source Quantitative Pathology & Bioimage Analysis DP system since its inception, by discussing the scope of scientific research in which QuPath has been cited, as the system of choice for researchers.
Indexed as
Identifiers
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.