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      Histological evaluation of PAXgene tissue fixation in Barrett’s esophagus and esophageal adenocarcinoma diagnostics

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          Abstract

          The dysplasia grading of Barrett’s esophagus (BE), based on the histomorphological assessment of formalin-fixed, paraffin-embedded (FFPE) tissue, suffers from high interobserver variability leading to an unsatisfactory prediction of cancer risk. Thus, pre-analytic preservation of biological molecules, which could improve risk prediction in BE enabling molecular and genetic analysis, is needed. We aimed to evaluate such a molecular pre-analytic fixation tool, PAXgene-fixed paraffin-embedded (PFPE) biopsies, and their suitability for histomorphological BE diagnostics in comparison to FFPE. In a ring trial, 9 GI pathologists evaluated 116 digital BE slides of non-dysplastic BE (NDBE), low-grade dysplasia (LGD), high-grade dysplasia (HGD), and esophageal adenocarcinomas (EAC) using virtual microscopy. Overall quality, cytological and histomorphological parameters, dysplasia criteria, and diagnosis were analyzed. PFPE showed better preservation of nuclear details as chromatin and nucleoli, whereas overall quality and histomorphologic parameters as visibility of basal lamina, goblet cells, and presence of artifacts were scored as equal to FFPE. The interobserver reproducibility with regard to the diagnosis was best for NDBE and EAC ( κ F  = 0.72–0.75) and poor for LGD and HGD ( κ F  = 0.13–0.3) in both. In conclusion, our data suggest that PFPE allows equally confident histomorphological diagnosis of BE and EAC, introducing a novel tool for molecular analysis and parallel histomorphological evaluation.

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          The Measurement of Observer Agreement for Categorical Data

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            Clinical-grade computational pathology using weakly supervised deep learning on whole slide images

            The development of decision support systems for pathology and their deployment in clinical practice have been hindered by the need for large manually annotated datasets. To overcome this problem, we present a multiple instance learning-based deep learning system that uses only the reported diagnoses as labels for training, thereby avoiding expensive and time-consuming pixel-wise manual annotations. We evaluated this framework at scale on a dataset of 44,732 whole slide images from 15,187 patients without any form of data curation. Tests on prostate cancer, basal cell carcinoma and breast cancer metastases to axillary lymph nodes resulted in areas under the curve above 0.98 for all cancer types. Its clinical application would allow pathologists to exclude 65–75% of slides while retaining 100% sensitivity. Our results show that this system has the ability to train accurate classification models at unprecedented scale, laying the foundation for the deployment of computational decision support systems in clinical practice.
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              Deep learning can predict microsatellite instability directly from histology in gastrointestinal cancer

              Microsatellite instability determines whether patients with gastrointestinal cancer respond exceptionally well to immunotherapy. However, in clinical practice, not every patient is tested for MSI, because this requires additional genetic or immunohistochemical tests. Here we show that deep residual learning can predict MSI directly from H&E histology, which is ubiquitously available. This approach has the potential to provide immunotherapy to a much broader subset of patients with gastrointestinal cancer.
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                Author and article information

                Contributors
                melissa.barroux@mri.tum.de
                Journal
                Virchows Arch
                Virchows Arch
                Virchows Archiv
                Springer Berlin Heidelberg (Berlin/Heidelberg )
                0945-6317
                1432-2307
                17 December 2022
                17 December 2022
                2023
                : 482
                : 5
                : 887-898
                Affiliations
                [1 ]GRID grid.6936.a, ISNI 0000000123222966, Klinikum Rechts Der Isar, , Medical Clinic and Polyclinic II, Technical University of Munich, ; Munich, Germany
                [2 ]GRID grid.7708.8, ISNI 0000 0000 9428 7911, Department of Medicine II, , Universitaetsklinikum Freiburg, ; Freiburg, Germany
                [3 ]GRID grid.5963.9, Institute for Surgical Pathology, , Medical Center-University of Freiburg and Faculty of Medicine, University of Freiburg, ; 79106 Freiburg, Germany
                [4 ]Department of Pathology, Yaroslavl Regional Cancer Hospital, Yaroslavl, Russian Federation
                [5 ]GRID grid.4491.8, ISNI 0000 0004 1937 116X, The Fingerland Department of Pathology, Faculty of Medicine and University Hospital, , Charles University, ; Hradec Králové, Czech Republic
                [6 ]GRID grid.412370.3, ISNI 0000 0004 1937 1100, Service d’Anatomie Pathologique, AP-HP, Faculté de Médecine Sorbonne, , Hôpital Saint-Antoine, Université, ; 75012 Paris, France
                [7 ]Institute of Pathology and Molecular Diagnostics, University Medical Center Augsburg, Augsburg, Germany
                [8 ]GRID grid.1012.2, ISNI 0000 0004 1936 7910, Department of Pathology, , PathWest Laboratory-University of Western Australia, ; WA Perth, Australia
                [9 ]GRID grid.5330.5, ISNI 0000 0001 2107 3311, Institute for Pathology, , Friedrich-Alexander-University Erlangen-Nuremberg, ; Klinikum Bayreuth, Bayreuth, Germany
                [10 ]GRID grid.412590.b, ISNI 0000 0000 9081 2336, Department of Pathology, , Michigan Medicine, ; Ann Arbor, MI USA
                [11 ]GRID grid.38142.3c, ISNI 000000041936754X, Department of Pathology, , Brigham and Women’s Hospital and Harvard Medical School, ; Boston, USA
                [12 ]GRID grid.6936.a, ISNI 0000000123222966, Institute of Pathology, , Technical University of Munich, ; Munich, Germany
                Author information
                http://orcid.org/0000-0002-7682-0932
                Article
                3471
                10.1007/s00428-022-03471-9
                10156762
                36527466
                63183a4e-f30b-4ace-a93d-d8a1fa36a9bc
                © The Author(s) 2022

                Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 16 August 2022
                : 1 November 2022
                : 30 November 2022
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100005972, Deutsche Krebshilfe;
                Award ID: 108296
                Award ID: 108296
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100002347, Bundesministerium für Bildung und Forschung;
                Award ID: 01EX1221B
                Award ID: PM25
                Award Recipient :
                Funded by: Klinikum rechts der Isar der Technischen Universität München (8934)
                Categories
                Original Article
                Custom metadata
                © Springer-Verlag GmbH Germany, part of Springer Nature 2023

                Pathology
                esophageal adenocarcinoma,dysplasia,paxgene-fixed paraffin-embedded
                Pathology
                esophageal adenocarcinoma, dysplasia, paxgene-fixed paraffin-embedded

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