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An international multi-institutional validation study of the algorithm for prostate cancer detection and Gleason grading

Pathologic examination of prostate biopsies is time consuming due to the large number of slides per case. In this retrospective study, we validate a deep learning-based classifier for prostate cancer (PCA) detection and Gleason grading (AI tool) in biopsy samples. Five external cohorts of patients w...

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Autores principales: Tolkach, Yuri, Ovtcharov, Vlado, Pryalukhin, Alexey, Eich, Marie-Lisa, Gaisa, Nadine Therese, Braun, Martin, Radzhabov, Abdukhamid, Quaas, Alexander, Hammerer, Peter, Dellmann, Ansgar, Hulla, Wolfgang, Haffner, Michael C., Reis, Henning, Fahoum, Ibrahim, Samarska, Iryna, Borbat, Artem, Pham, Hoa, Heidenreich, Axel, Klein, Sebastian, Netto, George, Caie, Peter, Buettner, Reinhard
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10427608/
https://www.ncbi.nlm.nih.gov/pubmed/37582946
http://dx.doi.org/10.1038/s41698-023-00424-6
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author Tolkach, Yuri
Ovtcharov, Vlado
Pryalukhin, Alexey
Eich, Marie-Lisa
Gaisa, Nadine Therese
Braun, Martin
Radzhabov, Abdukhamid
Quaas, Alexander
Hammerer, Peter
Dellmann, Ansgar
Hulla, Wolfgang
Haffner, Michael C.
Reis, Henning
Fahoum, Ibrahim
Samarska, Iryna
Borbat, Artem
Pham, Hoa
Heidenreich, Axel
Klein, Sebastian
Netto, George
Caie, Peter
Buettner, Reinhard
author_facet Tolkach, Yuri
Ovtcharov, Vlado
Pryalukhin, Alexey
Eich, Marie-Lisa
Gaisa, Nadine Therese
Braun, Martin
Radzhabov, Abdukhamid
Quaas, Alexander
Hammerer, Peter
Dellmann, Ansgar
Hulla, Wolfgang
Haffner, Michael C.
Reis, Henning
Fahoum, Ibrahim
Samarska, Iryna
Borbat, Artem
Pham, Hoa
Heidenreich, Axel
Klein, Sebastian
Netto, George
Caie, Peter
Buettner, Reinhard
author_sort Tolkach, Yuri
collection PubMed
description Pathologic examination of prostate biopsies is time consuming due to the large number of slides per case. In this retrospective study, we validate a deep learning-based classifier for prostate cancer (PCA) detection and Gleason grading (AI tool) in biopsy samples. Five external cohorts of patients with multifocal prostate biopsy were analyzed from high-volume pathology institutes. A total of 5922 H&E sections representing 7473 biopsy cores from 423 patient cases (digitized using three scanners) were assessed concerning tumor detection. Two tumor-bearing datasets (core n = 227 and 159) were graded by an international group of pathologists including expert urologic pathologists (n = 11) to validate the Gleason grading classifier. The sensitivity, specificity, and NPV for the detection of tumor-bearing biopsies was in a range of 0.971–1.000, 0.875–0.976, and 0.988–1.000, respectively, across the different test cohorts. In several biopsy slides tumor tissue was correctly detected by the AI tool that was initially missed by pathologists. Most false positive misclassifications represented lesions suspicious for carcinoma or cancer mimickers. The quadratically weighted kappa levels for Gleason grading agreement for single pathologists was 0.62–0.80 (0.77 for AI tool) and 0.64–0.76 (0.72 for AI tool) for the two grading datasets, respectively. In cases where consensus for grading was reached among pathologists, kappa levels for AI tool were 0.903 and 0.855. The PCA detection classifier showed high accuracy for PCA detection in biopsy cases during external validation, independent of the institute and scanner used. High levels of agreement for Gleason grading were indistinguishable between experienced genitourinary pathologists and the AI tool.
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spelling pubmed-104276082023-08-17 An international multi-institutional validation study of the algorithm for prostate cancer detection and Gleason grading Tolkach, Yuri Ovtcharov, Vlado Pryalukhin, Alexey Eich, Marie-Lisa Gaisa, Nadine Therese Braun, Martin Radzhabov, Abdukhamid Quaas, Alexander Hammerer, Peter Dellmann, Ansgar Hulla, Wolfgang Haffner, Michael C. Reis, Henning Fahoum, Ibrahim Samarska, Iryna Borbat, Artem Pham, Hoa Heidenreich, Axel Klein, Sebastian Netto, George Caie, Peter Buettner, Reinhard NPJ Precis Oncol Article Pathologic examination of prostate biopsies is time consuming due to the large number of slides per case. In this retrospective study, we validate a deep learning-based classifier for prostate cancer (PCA) detection and Gleason grading (AI tool) in biopsy samples. Five external cohorts of patients with multifocal prostate biopsy were analyzed from high-volume pathology institutes. A total of 5922 H&E sections representing 7473 biopsy cores from 423 patient cases (digitized using three scanners) were assessed concerning tumor detection. Two tumor-bearing datasets (core n = 227 and 159) were graded by an international group of pathologists including expert urologic pathologists (n = 11) to validate the Gleason grading classifier. The sensitivity, specificity, and NPV for the detection of tumor-bearing biopsies was in a range of 0.971–1.000, 0.875–0.976, and 0.988–1.000, respectively, across the different test cohorts. In several biopsy slides tumor tissue was correctly detected by the AI tool that was initially missed by pathologists. Most false positive misclassifications represented lesions suspicious for carcinoma or cancer mimickers. The quadratically weighted kappa levels for Gleason grading agreement for single pathologists was 0.62–0.80 (0.77 for AI tool) and 0.64–0.76 (0.72 for AI tool) for the two grading datasets, respectively. In cases where consensus for grading was reached among pathologists, kappa levels for AI tool were 0.903 and 0.855. The PCA detection classifier showed high accuracy for PCA detection in biopsy cases during external validation, independent of the institute and scanner used. High levels of agreement for Gleason grading were indistinguishable between experienced genitourinary pathologists and the AI tool. Nature Publishing Group UK 2023-08-15 /pmc/articles/PMC10427608/ /pubmed/37582946 http://dx.doi.org/10.1038/s41698-023-00424-6 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This 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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Tolkach, Yuri
Ovtcharov, Vlado
Pryalukhin, Alexey
Eich, Marie-Lisa
Gaisa, Nadine Therese
Braun, Martin
Radzhabov, Abdukhamid
Quaas, Alexander
Hammerer, Peter
Dellmann, Ansgar
Hulla, Wolfgang
Haffner, Michael C.
Reis, Henning
Fahoum, Ibrahim
Samarska, Iryna
Borbat, Artem
Pham, Hoa
Heidenreich, Axel
Klein, Sebastian
Netto, George
Caie, Peter
Buettner, Reinhard
An international multi-institutional validation study of the algorithm for prostate cancer detection and Gleason grading
title An international multi-institutional validation study of the algorithm for prostate cancer detection and Gleason grading
title_full An international multi-institutional validation study of the algorithm for prostate cancer detection and Gleason grading
title_fullStr An international multi-institutional validation study of the algorithm for prostate cancer detection and Gleason grading
title_full_unstemmed An international multi-institutional validation study of the algorithm for prostate cancer detection and Gleason grading
title_short An international multi-institutional validation study of the algorithm for prostate cancer detection and Gleason grading
title_sort international multi-institutional validation study of the algorithm for prostate cancer detection and gleason grading
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10427608/
https://www.ncbi.nlm.nih.gov/pubmed/37582946
http://dx.doi.org/10.1038/s41698-023-00424-6
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