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Diagnostic concordance and discordance in digital pathology : a systematic review and meta-analysis
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Azam, Ayesha, Miligy, Islam, Kimani, Peter K., Maqbool, Heeba, Hewitt, Katherine, Rajpoot, Nasir M. and Snead, David (2020) Diagnostic concordance and discordance in digital pathology : a systematic review and meta-analysis. Journal of Clinical Pathology . doi:10.1136/jclinpath-2020-206764 (In Press)
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Official URL: https://doi.org/10.1136/jclinpath-2020-206764
Abstract
Background – Digital pathology (DP) has the potential to fundamentally change the way that histopathology is practiced, by streamlining the workflow, increasing efficiency, improving diagnostic accuracy and facilitating the platform for implementation of artificial intelligence-based computerassisted diagnostics. Although the barriers to wider adoption of digital pathology 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. Objectives – We 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. Methods – A 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. Results – Twenty 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% Confidence interval: 97.4 – 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%). Conclusion - The results of this meta-analysis indicate equivalent performance of DP in comparison to 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.
| Item Type: | Journal Article | |||||||||
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| Subjects: | R Medicine > R Medicine (General) R Medicine > RB Pathology R Medicine > RC Internal medicine |
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| Divisions: | Faculty of Science > Computer Science Faculty of Medicine > Warwick Medical School > Health Sciences Faculty of Medicine > Warwick Medical School > Health Sciences > Statistics and Epidemiology Faculty of Medicine > Warwick Medical School |
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| Library of Congress Subject Headings (LCSH): | Pathology -- Data processing, Medical informatics, Diagnosis -- Data processing, Histology, Pathological -- Computer programs | |||||||||
| Journal or Publication Title: | Journal of Clinical Pathology | |||||||||
| Publisher: | B M J Group | |||||||||
| ISSN: | 0021-9746 | |||||||||
| Official Date: | 15 September 2020 | |||||||||
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| Date of first compliant deposit: | 3 August 2020 | |||||||||
| DOI: | 10.1136/jclinpath-2020-206764 | |||||||||
| Status: | Peer Reviewed | |||||||||
| Publication Status: | In Press | |||||||||
| Access rights to Published version: | Open Access | |||||||||
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