Publicação
Quantitative assessment of myocardial fibrosis by digital image analysis: an adjunctive tool for pathologist “ground truth”
| dc.contributor.author | Abecasis, João | |
| dc.contributor.author | Cortez-Dias, Nuno | |
| dc.contributor.author | Pinto, Daniel Gomes | |
| dc.contributor.author | Lopes, Pedro | |
| dc.contributor.author | Madeira, Márcio | |
| dc.contributor.author | Ramos, Sancia | |
| dc.contributor.author | Gil, Victor | |
| dc.contributor.author | Cardim, Nuno | |
| dc.contributor.author | Félix, Ana | |
| dc.date.accessioned | 2023-07-31T12:51:53Z | |
| dc.date.available | 2023-07-31T12:51:53Z | |
| dc.date.issued | 2023 | |
| dc.description | © 2023 Elsevier Inc. All rights reserved. | pt_PT |
| dc.description.abstract | Aims: Myocardial fibrosis (MF) is a common pathological process in a wide range of cardiovascular diseases. Its quantity has diagnostic and prognostic relevance. We aimed to assess if the complementary use of an automated artificial intelligence software might improve the precision of the pathologist´s quantification of MF on endomyocardial biopsies (EMB). Methods and results: Intraoperative EMB samples from 30 patients with severe aortic stenosis submitted to surgical aortic valve replacement were analysed. Tissue sections were stained with Masson´s trichrome for collagen/fibrosis and whole slide images (WSI) from the experimental glass slides were obtained at a resolution of 0.5 μm using a digital microscopic scanner. Three experienced pathologists made a first quantification of MF excluding the subendocardium. After two weeks, an algorithm for Masson´s trichrome brightfield WSI (at QuPath software) was applied and the automatic quantification was revealed to the pathologists, who were asked to reassess MF, blinded to their first evaluation. The impact of the automatic algorithm on the inter-observer agreement was evaluated using Bland-Altman type methodology. Median values of MF on EMB were 8.33% [IQR 5.00-12.08%] and 13.60% [IQR 7.32-21.2%], respectively for the first pathologist´s and automatic algorithm quantification, being highly correlated (R2: 0.79; p < 0.001). Interobserver discordance was relevant, particularly for higher percentages of MF. The knowledge of the automatic quantification significantly improved the overall pathologist´s agreement, which became unaffected by the degree of MF severity. Conclusions: The use of an automated artificial intelligence software for MF quantification on EMB samples improves the reproducibility of measurements by experienced pathologists. By improving the reliability of the quantification of myocardial tissue components, this adjunctive tool may facilitate the implementation of imaging-pathology correlation studies. | pt_PT |
| dc.description.version | info:eu-repo/semantics/publishedVersion | pt_PT |
| dc.identifier.citation | Cardiovasc Pathol. 2023 Jul-Aug;65:107541 | pt_PT |
| dc.identifier.doi | 10.1016/j.carpath.2023.107541 | pt_PT |
| dc.identifier.eissn | 1879-1336 | |
| dc.identifier.issn | 1054-8807 | |
| dc.identifier.uri | http://hdl.handle.net/10451/58797 | |
| dc.language.iso | eng | pt_PT |
| dc.peerreviewed | yes | pt_PT |
| dc.publisher | Elsevier | pt_PT |
| dc.relation.publisherversion | https://www.sciencedirect.com/journal/cardiovascular-pathology | pt_PT |
| dc.subject | Automatic quantification algorithms | pt_PT |
| dc.subject | Digital pathology | pt_PT |
| dc.subject | Machine learning | pt_PT |
| dc.subject | Myocardial fibrosis | pt_PT |
| dc.title | Quantitative assessment of myocardial fibrosis by digital image analysis: an adjunctive tool for pathologist “ground truth” | pt_PT |
| dc.type | journal article | |
| dspace.entity.type | Publication | |
| oaire.citation.title | Cardiovascular Pathology | pt_PT |
| oaire.citation.volume | 65 | pt_PT |
| person.familyName | Cortez-Dias | |
| person.givenName | Nuno | |
| person.identifier.ciencia-id | D612-D3C3-6E1D | |
| person.identifier.orcid | 0000-0002-9244-4631 | |
| rcaap.rights | restrictedAccess | pt_PT |
| rcaap.type | article | pt_PT |
| relation.isAuthorOfPublication | 36f4a32d-38e4-4c28-9140-4ba468059a22 | |
| relation.isAuthorOfPublication.latestForDiscovery | 36f4a32d-38e4-4c28-9140-4ba468059a22 |