Cargando…
Next-Generation Morphometry for pathomics-data mining in histopathology
Pathology diagnostics relies on the assessment of morphology by trained experts, which remains subjective and qualitative. Here we developed a framework for large-scale histomorphometry (FLASH) performing deep learning-based semantic segmentation and subsequent large-scale extraction of interpretabl...
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/PMC9884209/ https://www.ncbi.nlm.nih.gov/pubmed/36709324 http://dx.doi.org/10.1038/s41467-023-36173-0 |
_version_ | 1784879668078313472 |
---|---|
author | Hölscher, David L. Bouteldja, Nassim Joodaki, Mehdi Russo, Maria L. Lan, Yu-Chia Sadr, Alireza Vafaei Cheng, Mingbo Tesar, Vladimir Stillfried, Saskia V. Klinkhammer, Barbara M. Barratt, Jonathan Floege, Jürgen Roberts, Ian S. D. Coppo, Rosanna Costa, Ivan G. Bülow, Roman D. Boor, Peter |
author_facet | Hölscher, David L. Bouteldja, Nassim Joodaki, Mehdi Russo, Maria L. Lan, Yu-Chia Sadr, Alireza Vafaei Cheng, Mingbo Tesar, Vladimir Stillfried, Saskia V. Klinkhammer, Barbara M. Barratt, Jonathan Floege, Jürgen Roberts, Ian S. D. Coppo, Rosanna Costa, Ivan G. Bülow, Roman D. Boor, Peter |
author_sort | Hölscher, David L. |
collection | PubMed |
description | Pathology diagnostics relies on the assessment of morphology by trained experts, which remains subjective and qualitative. Here we developed a framework for large-scale histomorphometry (FLASH) performing deep learning-based semantic segmentation and subsequent large-scale extraction of interpretable, quantitative, morphometric features in non-tumour kidney histology. We use two internal and three external, multi-centre cohorts to analyse over 1000 kidney biopsies and nephrectomies. By associating morphometric features with clinical parameters, we confirm previous concepts and reveal unexpected relations. We show that the extracted features are independent predictors of long-term clinical outcomes in IgA-nephropathy. We introduce single-structure morphometric analysis by applying techniques from single-cell transcriptomics, identifying distinct glomerular populations and morphometric phenotypes along a trajectory of disease progression. Our study provides a concept for Next-generation Morphometry (NGM), enabling comprehensive quantitative pathology data mining, i.e., pathomics. |
format | Online Article Text |
id | pubmed-9884209 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-98842092023-01-30 Next-Generation Morphometry for pathomics-data mining in histopathology Hölscher, David L. Bouteldja, Nassim Joodaki, Mehdi Russo, Maria L. Lan, Yu-Chia Sadr, Alireza Vafaei Cheng, Mingbo Tesar, Vladimir Stillfried, Saskia V. Klinkhammer, Barbara M. Barratt, Jonathan Floege, Jürgen Roberts, Ian S. D. Coppo, Rosanna Costa, Ivan G. Bülow, Roman D. Boor, Peter Nat Commun Article Pathology diagnostics relies on the assessment of morphology by trained experts, which remains subjective and qualitative. Here we developed a framework for large-scale histomorphometry (FLASH) performing deep learning-based semantic segmentation and subsequent large-scale extraction of interpretable, quantitative, morphometric features in non-tumour kidney histology. We use two internal and three external, multi-centre cohorts to analyse over 1000 kidney biopsies and nephrectomies. By associating morphometric features with clinical parameters, we confirm previous concepts and reveal unexpected relations. We show that the extracted features are independent predictors of long-term clinical outcomes in IgA-nephropathy. We introduce single-structure morphometric analysis by applying techniques from single-cell transcriptomics, identifying distinct glomerular populations and morphometric phenotypes along a trajectory of disease progression. Our study provides a concept for Next-generation Morphometry (NGM), enabling comprehensive quantitative pathology data mining, i.e., pathomics. Nature Publishing Group UK 2023-01-28 /pmc/articles/PMC9884209/ /pubmed/36709324 http://dx.doi.org/10.1038/s41467-023-36173-0 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 Hölscher, David L. Bouteldja, Nassim Joodaki, Mehdi Russo, Maria L. Lan, Yu-Chia Sadr, Alireza Vafaei Cheng, Mingbo Tesar, Vladimir Stillfried, Saskia V. Klinkhammer, Barbara M. Barratt, Jonathan Floege, Jürgen Roberts, Ian S. D. Coppo, Rosanna Costa, Ivan G. Bülow, Roman D. Boor, Peter Next-Generation Morphometry for pathomics-data mining in histopathology |
title | Next-Generation Morphometry for pathomics-data mining in histopathology |
title_full | Next-Generation Morphometry for pathomics-data mining in histopathology |
title_fullStr | Next-Generation Morphometry for pathomics-data mining in histopathology |
title_full_unstemmed | Next-Generation Morphometry for pathomics-data mining in histopathology |
title_short | Next-Generation Morphometry for pathomics-data mining in histopathology |
title_sort | next-generation morphometry for pathomics-data mining in histopathology |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9884209/ https://www.ncbi.nlm.nih.gov/pubmed/36709324 http://dx.doi.org/10.1038/s41467-023-36173-0 |
work_keys_str_mv | AT holscherdavidl nextgenerationmorphometryforpathomicsdatamininginhistopathology AT bouteldjanassim nextgenerationmorphometryforpathomicsdatamininginhistopathology AT joodakimehdi nextgenerationmorphometryforpathomicsdatamininginhistopathology AT russomarial nextgenerationmorphometryforpathomicsdatamininginhistopathology AT lanyuchia nextgenerationmorphometryforpathomicsdatamininginhistopathology AT sadralirezavafaei nextgenerationmorphometryforpathomicsdatamininginhistopathology AT chengmingbo nextgenerationmorphometryforpathomicsdatamininginhistopathology AT tesarvladimir nextgenerationmorphometryforpathomicsdatamininginhistopathology AT stillfriedsaskiav nextgenerationmorphometryforpathomicsdatamininginhistopathology AT klinkhammerbarbaram nextgenerationmorphometryforpathomicsdatamininginhistopathology AT barrattjonathan nextgenerationmorphometryforpathomicsdatamininginhistopathology AT floegejurgen nextgenerationmorphometryforpathomicsdatamininginhistopathology AT robertsiansd nextgenerationmorphometryforpathomicsdatamininginhistopathology AT copporosanna nextgenerationmorphometryforpathomicsdatamininginhistopathology AT costaivang nextgenerationmorphometryforpathomicsdatamininginhistopathology AT bulowromand nextgenerationmorphometryforpathomicsdatamininginhistopathology AT boorpeter nextgenerationmorphometryforpathomicsdatamininginhistopathology |