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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 (2021) Diagnostic concordance and discordance in digital pathology : a systematic review and meta-analysis. Journal of Clinical Pathology, 74 . pp. 448-455. doi:10.1136/jclinpath-2020-206764 ISSN 0021-9746.

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Official URL: https://doi.org/10.1136/jclinpath-2020-206764

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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
Alternative Title:
Subjects: R Medicine > R Medicine (General)
R Medicine > RB Pathology
R Medicine > RC Internal medicine
Divisions: Faculty of Science, Engineering and Medicine > Science > Computer Science
Faculty of Science, Engineering and Medicine > Medicine > Warwick Medical School > Health Sciences
Faculty of Science, Engineering and Medicine > Medicine > Warwick Medical School > Health Sciences > Statistics and Epidemiology
Faculty of Science, Engineering and Medicine > Medicine > Warwick Medical School
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: 18 June 2021
Dates:
DateEvent
18 June 2021Published
15 September 2020Available
10 July 2020Accepted
Volume: 74
Page Range: pp. 448-455
DOI: 10.1136/jclinpath-2020-206764
Status: Peer Reviewed
Publication Status: Published
Access rights to Published version: Open Access (Creative Commons)
Date of first compliant deposit: 3 August 2020
Date of first compliant Open Access: 23 September 2020
RIOXX Funder/Project Grant:
Project/Grant IDRIOXX Funder NameFunder ID
18181 Innovate UKhttp://dx.doi.org/10.13039/501100006041
17/84/07 Ref 126020[NIHR] National Institute for Health Researchhttp://dx.doi.org/10.13039/501100000272
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