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Diagnostic concordance and discordance in digital pathology: a systematic review and meta-analysis
BACKGROUND: Digital 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-assiste...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BMJ Publishing Group
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8223673/ https://www.ncbi.nlm.nih.gov/pubmed/32934103 http://dx.doi.org/10.1136/jclinpath-2020-206764 |
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author | Azam, Ayesha S Miligy, Islam M Kimani, Peter K-U Maqbool, Heeba Hewitt, Katherine Rajpoot, Nasir M Snead, David R J |
author_facet | Azam, Ayesha S Miligy, Islam M Kimani, Peter K-U Maqbool, Heeba Hewitt, Katherine Rajpoot, Nasir M Snead, David R J |
author_sort | Azam, Ayesha S |
collection | PubMed |
description | BACKGROUND: Digital 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. 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% 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%). CONCLUSION: The 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. |
format | Online Article Text |
id | pubmed-8223673 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | BMJ Publishing Group |
record_format | MEDLINE/PubMed |
spelling | pubmed-82236732021-07-09 Diagnostic concordance and discordance in digital pathology: a systematic review and meta-analysis Azam, Ayesha S Miligy, Islam M Kimani, Peter K-U Maqbool, Heeba Hewitt, Katherine Rajpoot, Nasir M Snead, David R J J Clin Pathol Review BACKGROUND: Digital 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. 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% 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%). CONCLUSION: The 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. BMJ Publishing Group 2021-07 2020-09-15 /pmc/articles/PMC8223673/ /pubmed/32934103 http://dx.doi.org/10.1136/jclinpath-2020-206764 Text en © Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY. Published by BMJ. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed in accordance with the Creative Commons Attribution 4.0 Unported (CC BY 4.0) license, which permits others to copy, redistribute, remix, transform and build upon this work for any purpose, provided the original work is properly cited, a link to the licence is given, and indication of whether changes were made. See: https://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Review Azam, Ayesha S Miligy, Islam M Kimani, Peter K-U Maqbool, Heeba Hewitt, Katherine Rajpoot, Nasir M Snead, David R J Diagnostic concordance and discordance in digital pathology: a systematic review and meta-analysis |
title | Diagnostic concordance and discordance in digital pathology: a systematic review and meta-analysis |
title_full | Diagnostic concordance and discordance in digital pathology: a systematic review and meta-analysis |
title_fullStr | Diagnostic concordance and discordance in digital pathology: a systematic review and meta-analysis |
title_full_unstemmed | Diagnostic concordance and discordance in digital pathology: a systematic review and meta-analysis |
title_short | Diagnostic concordance and discordance in digital pathology: a systematic review and meta-analysis |
title_sort | diagnostic concordance and discordance in digital pathology: a systematic review and meta-analysis |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8223673/ https://www.ncbi.nlm.nih.gov/pubmed/32934103 http://dx.doi.org/10.1136/jclinpath-2020-206764 |
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