SynthesisJournal of clinical pathology2021
Diagnostic concordance and discordance in digital pathology: a systematic review and meta-analysis.
Synthesis in Journal of clinical pathology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers, 1 of them a synthesis that pooled it.
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
34 citing papers in PubMed, 1 synthesis or guideline pooled it, 82 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
- Validation of Digital Cytology for Primary Diagnosis Across a Range of Specimen Types.Cytopathology : official journal of the British Society for Clinical Cytology · 2026Article
- Leveraging digital pathology to enhance breast cancer diagnosis in low-resource settings: a cross-sectional study of major histopathology laboratories in Uganda.Oxford open digital health · 2026Article
- Roche Digital Pathology Dx whole slide imaging system is comparable to traditional microscopy for primary diagnosis in surgical pathology.American journal of clinical pathology · 2025Article
- Variation within and between digital pathology and light microscopy for the diagnosis of histopathology slides: blinded crossover comparison study.Health technology assessment (Winchester, England) · 2025Article
- What factors influence cellular pathologists' confidence in case reporting?Virchows Archiv : an international journal of pathology · 2025Article
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- Article
- Digital Pathology Allows for Global Second Opinions for Urologic Malignancies.Current urology reports · 2025Review
- Performance of externally validated machine learning models based on histopathology images for the diagnosis, classification, prognosis, or treatment outcome prediction in female breast cancer: A systematic review.Journal of pathology informatics · 2024Review
- NMGrad: Advancing Histopathological Bladder Cancer Grading with Weakly Supervised Deep Learning.Bioengineering (Basel, Switzerland) · 2024Article
- Digital pathology, deep learning, and cancer: a narrative review.Translational cancer research · 2024Review
- An Experimental Platform for Tomographic Reconstruction of Tissue Images in Brightfield Microscopy.Sensors (Basel, Switzerland) · 2023Article
- Digital pathology systems enabling quality patient care.Genes, chromosomes & cancer · 2023Review
- Impact of the transition to digital pathology in a clinical setting on histopathologists in training: experiences and perceived challenges within a UK training region.Journal of clinical pathology · 2023Article
- Integrating cytology into routine digital pathology workflow: a 5-year journey.Virchows Archiv : an international journal of pathology · 2023Article
- Prospective on Imaging Mass Spectrometry in Clinical Diagnostics.Molecular & cellular proteomics : MCP · 2023Review
- Department Wide Validation in Digital Pathology-Experience from an Academic Teaching Hospital Using the UK Royal College of Pathologists' Guidance.Diagnostics (Basel, Switzerland) · 2023Article
- Histopathological evaluation ofFrontiers in medicine · 2023Article
- Fast and scalable search of whole-slide images via self-supervised deep learning.Nature biomedical engineering · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 3 institutions in 1 country.
Funding
Abstract
backgroundDigital pathology (DP) has the potential to fundamentally change the way that histopathology is practised, by streamlining the workflow, increasing efficiency, improving diagnostic accuracy and facilitating the platform for implementation of artificial intelligence-based computer-assisted diagnostics. Although the barriers to wider adoption of DP have been multifactorial, limited evidence of reliability has been a significant contributor. A meta-analysis to demonstrate the combined accuracy and reliability of DP is still lacking in the literature.
objectivesWe aimed to review the published literature on the diagnostic use of DP and to synthesise a statistically pooled evidence on safety and reliability of DP for routine diagnosis (primary and secondary) in the context of validation process.
methodsA comprehensive literature search was conducted through PubMed, Medline, EMBASE, Cochrane Library and Google Scholar for studies published between 2013 and August 2019. The search protocol identified all studies comparing DP with light microscopy (LM) reporting for diagnostic purposes, predominantly including H&E-stained slides. Random-effects meta-analysis was used to pool evidence from the studies.
resultsTwenty-five studies were deemed eligible to be included in the review which examined a total of 10 410 histology samples (average sample size 176). For overall concordance (clinical concordance), the agreement percentage was 98.3% (95% CI 97.4 to 98.9) across 24 studies. A total of 546 major discordances were reported across 25 studies. Over half (57%) of these were related to assessment of nuclear atypia, grading of dysplasia and malignancy. These were followed by challenging diagnoses (26%) and identification of small objects (16%).
conclusionThe results of this meta-analysis indicate equivalent performance of DP in comparison with LM for routine diagnosis. Furthermore, the results provide valuable information concerning the areas of diagnostic discrepancy which may warrant particular attention in the transition to DP.
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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 Socratic graph.