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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...

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Autores principales: 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
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
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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.
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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
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