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QuPath Algorithm Accurately Identifies MLH1-Deficient Inflammatory Bowel Disease-Associated Colorectal Cancers in a Tissue Microarray

Current methods for analysing immunohistochemistry are labour-intensive and often confounded by inter-observer variability. Analysis is time consuming when identifying small clinically important cohorts within larger samples. This study trained QuPath, an open-source image analysis program, to accur...

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Autores principales: Porter, Ross J., Din, Shahida, Bankhead, Peter, Oniscu, Anca, Arends, Mark J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10253133/
https://www.ncbi.nlm.nih.gov/pubmed/37296742
http://dx.doi.org/10.3390/diagnostics13111890
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author Porter, Ross J.
Din, Shahida
Bankhead, Peter
Oniscu, Anca
Arends, Mark J.
author_facet Porter, Ross J.
Din, Shahida
Bankhead, Peter
Oniscu, Anca
Arends, Mark J.
author_sort Porter, Ross J.
collection PubMed
description Current methods for analysing immunohistochemistry are labour-intensive and often confounded by inter-observer variability. Analysis is time consuming when identifying small clinically important cohorts within larger samples. This study trained QuPath, an open-source image analysis program, to accurately identify MLH1-deficient inflammatory bowel disease-associated colorectal cancers (IBD-CRC) from a tissue microarray containing normal colon and IBD-CRC. The tissue microarray (n = 162 cores) was immunostained for MLH1, digitalised, and imported into QuPath. A small sample (n = 14) was used to train QuPath to detect positive versus no MLH1 and tissue histology (normal epithelium, tumour, immune infiltrates, stroma). This algorithm was applied to the tissue microarray and correctly identified tissue histology and MLH1 expression in the majority of valid cases (73/99, 73.74%), incorrectly identified MLH1 status in one case (1.01%), and flagged 25/99 (25.25%) cases for manual review. Qualitative review found five reasons for flagged cores: small quantity of tissue, diverse/atypical morphology, excessive inflammatory/immune infiltrations, normal mucosa, or weak/patchy immunostaining. Of classified cores (n = 74), QuPath was 100% (95% CI 80.49, 100) sensitive and 98.25% (95% CI 90.61, 99.96) specific for identifying MLH1-deficient IBD-CRC; κ = 0.963 (95% CI 0.890, 1.036) (p < 0.001). This process could be efficiently automated in diagnostic laboratories to examine all colonic tissue and tumours for MLH1 expression.
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spelling pubmed-102531332023-06-10 QuPath Algorithm Accurately Identifies MLH1-Deficient Inflammatory Bowel Disease-Associated Colorectal Cancers in a Tissue Microarray Porter, Ross J. Din, Shahida Bankhead, Peter Oniscu, Anca Arends, Mark J. Diagnostics (Basel) Article Current methods for analysing immunohistochemistry are labour-intensive and often confounded by inter-observer variability. Analysis is time consuming when identifying small clinically important cohorts within larger samples. This study trained QuPath, an open-source image analysis program, to accurately identify MLH1-deficient inflammatory bowel disease-associated colorectal cancers (IBD-CRC) from a tissue microarray containing normal colon and IBD-CRC. The tissue microarray (n = 162 cores) was immunostained for MLH1, digitalised, and imported into QuPath. A small sample (n = 14) was used to train QuPath to detect positive versus no MLH1 and tissue histology (normal epithelium, tumour, immune infiltrates, stroma). This algorithm was applied to the tissue microarray and correctly identified tissue histology and MLH1 expression in the majority of valid cases (73/99, 73.74%), incorrectly identified MLH1 status in one case (1.01%), and flagged 25/99 (25.25%) cases for manual review. Qualitative review found five reasons for flagged cores: small quantity of tissue, diverse/atypical morphology, excessive inflammatory/immune infiltrations, normal mucosa, or weak/patchy immunostaining. Of classified cores (n = 74), QuPath was 100% (95% CI 80.49, 100) sensitive and 98.25% (95% CI 90.61, 99.96) specific for identifying MLH1-deficient IBD-CRC; κ = 0.963 (95% CI 0.890, 1.036) (p < 0.001). This process could be efficiently automated in diagnostic laboratories to examine all colonic tissue and tumours for MLH1 expression. MDPI 2023-05-28 /pmc/articles/PMC10253133/ /pubmed/37296742 http://dx.doi.org/10.3390/diagnostics13111890 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Porter, Ross J.
Din, Shahida
Bankhead, Peter
Oniscu, Anca
Arends, Mark J.
QuPath Algorithm Accurately Identifies MLH1-Deficient Inflammatory Bowel Disease-Associated Colorectal Cancers in a Tissue Microarray
title QuPath Algorithm Accurately Identifies MLH1-Deficient Inflammatory Bowel Disease-Associated Colorectal Cancers in a Tissue Microarray
title_full QuPath Algorithm Accurately Identifies MLH1-Deficient Inflammatory Bowel Disease-Associated Colorectal Cancers in a Tissue Microarray
title_fullStr QuPath Algorithm Accurately Identifies MLH1-Deficient Inflammatory Bowel Disease-Associated Colorectal Cancers in a Tissue Microarray
title_full_unstemmed QuPath Algorithm Accurately Identifies MLH1-Deficient Inflammatory Bowel Disease-Associated Colorectal Cancers in a Tissue Microarray
title_short QuPath Algorithm Accurately Identifies MLH1-Deficient Inflammatory Bowel Disease-Associated Colorectal Cancers in a Tissue Microarray
title_sort qupath algorithm accurately identifies mlh1-deficient inflammatory bowel disease-associated colorectal cancers in a tissue microarray
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10253133/
https://www.ncbi.nlm.nih.gov/pubmed/37296742
http://dx.doi.org/10.3390/diagnostics13111890
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