Cargando…
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...
Autores principales: | , , , , , , , , , , , , , , , , , , , , , |
---|---|
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 |
_version_ | 1785090275697229824 |
---|---|
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. |
format | Online Article Text |
id | pubmed-10427608 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT tolkachyuri aninternationalmultiinstitutionalvalidationstudyofthealgorithmforprostatecancerdetectionandgleasongrading AT ovtcharovvlado aninternationalmultiinstitutionalvalidationstudyofthealgorithmforprostatecancerdetectionandgleasongrading AT pryalukhinalexey aninternationalmultiinstitutionalvalidationstudyofthealgorithmforprostatecancerdetectionandgleasongrading AT eichmarielisa aninternationalmultiinstitutionalvalidationstudyofthealgorithmforprostatecancerdetectionandgleasongrading AT gaisanadinetherese aninternationalmultiinstitutionalvalidationstudyofthealgorithmforprostatecancerdetectionandgleasongrading AT braunmartin aninternationalmultiinstitutionalvalidationstudyofthealgorithmforprostatecancerdetectionandgleasongrading AT radzhabovabdukhamid aninternationalmultiinstitutionalvalidationstudyofthealgorithmforprostatecancerdetectionandgleasongrading AT quaasalexander aninternationalmultiinstitutionalvalidationstudyofthealgorithmforprostatecancerdetectionandgleasongrading AT hammererpeter aninternationalmultiinstitutionalvalidationstudyofthealgorithmforprostatecancerdetectionandgleasongrading AT dellmannansgar aninternationalmultiinstitutionalvalidationstudyofthealgorithmforprostatecancerdetectionandgleasongrading AT hullawolfgang aninternationalmultiinstitutionalvalidationstudyofthealgorithmforprostatecancerdetectionandgleasongrading AT haffnermichaelc aninternationalmultiinstitutionalvalidationstudyofthealgorithmforprostatecancerdetectionandgleasongrading AT reishenning aninternationalmultiinstitutionalvalidationstudyofthealgorithmforprostatecancerdetectionandgleasongrading AT fahoumibrahim aninternationalmultiinstitutionalvalidationstudyofthealgorithmforprostatecancerdetectionandgleasongrading AT samarskairyna aninternationalmultiinstitutionalvalidationstudyofthealgorithmforprostatecancerdetectionandgleasongrading AT borbatartem aninternationalmultiinstitutionalvalidationstudyofthealgorithmforprostatecancerdetectionandgleasongrading AT phamhoa aninternationalmultiinstitutionalvalidationstudyofthealgorithmforprostatecancerdetectionandgleasongrading AT heidenreichaxel aninternationalmultiinstitutionalvalidationstudyofthealgorithmforprostatecancerdetectionandgleasongrading AT kleinsebastian aninternationalmultiinstitutionalvalidationstudyofthealgorithmforprostatecancerdetectionandgleasongrading AT nettogeorge aninternationalmultiinstitutionalvalidationstudyofthealgorithmforprostatecancerdetectionandgleasongrading AT caiepeter aninternationalmultiinstitutionalvalidationstudyofthealgorithmforprostatecancerdetectionandgleasongrading AT buettnerreinhard aninternationalmultiinstitutionalvalidationstudyofthealgorithmforprostatecancerdetectionandgleasongrading AT tolkachyuri internationalmultiinstitutionalvalidationstudyofthealgorithmforprostatecancerdetectionandgleasongrading AT ovtcharovvlado internationalmultiinstitutionalvalidationstudyofthealgorithmforprostatecancerdetectionandgleasongrading AT pryalukhinalexey internationalmultiinstitutionalvalidationstudyofthealgorithmforprostatecancerdetectionandgleasongrading AT eichmarielisa internationalmultiinstitutionalvalidationstudyofthealgorithmforprostatecancerdetectionandgleasongrading AT gaisanadinetherese internationalmultiinstitutionalvalidationstudyofthealgorithmforprostatecancerdetectionandgleasongrading AT braunmartin internationalmultiinstitutionalvalidationstudyofthealgorithmforprostatecancerdetectionandgleasongrading AT radzhabovabdukhamid internationalmultiinstitutionalvalidationstudyofthealgorithmforprostatecancerdetectionandgleasongrading AT quaasalexander internationalmultiinstitutionalvalidationstudyofthealgorithmforprostatecancerdetectionandgleasongrading AT hammererpeter internationalmultiinstitutionalvalidationstudyofthealgorithmforprostatecancerdetectionandgleasongrading AT dellmannansgar internationalmultiinstitutionalvalidationstudyofthealgorithmforprostatecancerdetectionandgleasongrading AT hullawolfgang internationalmultiinstitutionalvalidationstudyofthealgorithmforprostatecancerdetectionandgleasongrading AT haffnermichaelc internationalmultiinstitutionalvalidationstudyofthealgorithmforprostatecancerdetectionandgleasongrading AT reishenning internationalmultiinstitutionalvalidationstudyofthealgorithmforprostatecancerdetectionandgleasongrading AT fahoumibrahim internationalmultiinstitutionalvalidationstudyofthealgorithmforprostatecancerdetectionandgleasongrading AT samarskairyna internationalmultiinstitutionalvalidationstudyofthealgorithmforprostatecancerdetectionandgleasongrading AT borbatartem internationalmultiinstitutionalvalidationstudyofthealgorithmforprostatecancerdetectionandgleasongrading AT phamhoa internationalmultiinstitutionalvalidationstudyofthealgorithmforprostatecancerdetectionandgleasongrading AT heidenreichaxel internationalmultiinstitutionalvalidationstudyofthealgorithmforprostatecancerdetectionandgleasongrading AT kleinsebastian internationalmultiinstitutionalvalidationstudyofthealgorithmforprostatecancerdetectionandgleasongrading AT nettogeorge internationalmultiinstitutionalvalidationstudyofthealgorithmforprostatecancerdetectionandgleasongrading AT caiepeter internationalmultiinstitutionalvalidationstudyofthealgorithmforprostatecancerdetectionandgleasongrading AT buettnerreinhard internationalmultiinstitutionalvalidationstudyofthealgorithmforprostatecancerdetectionandgleasongrading |