Next-Generation Morphometry for pathomics-data mining in histopathology
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00064165%3A_____%2F23%3A10464665" target="_blank" >RIV/00064165:_____/23:10464665 - isvavai.cz</a>
Alternative codes found
RIV/00216208:11110/23:10464665
Result on the web
<a href="https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=qWB0fnf8j1" target="_blank" >https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=qWB0fnf8j1</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1038/s41467-023-36173-0" target="_blank" >10.1038/s41467-023-36173-0</a>
Alternative languages
Result language
angličtina
Original language name
Next-Generation Morphometry for pathomics-data mining in histopathology
Original language 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. Pathology diagnostics still rely on tissue morphology assessment by trained experts. Here, the authors perform deep-learning-based segmentation followed by large-scale feature extraction of histological images, i.e., next-generation morphometry, to enable outcome-relevant and disease-specific pathomics analysis of non-tumor kidney pathology.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
30217 - Urology and nephrology
Result continuities
Project
—
Continuities
V - Vyzkumna aktivita podporovana z jinych verejnych zdroju
Others
Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Nature Communications
ISSN
2041-1723
e-ISSN
2041-1723
Volume of the periodical
14
Issue of the periodical within the volume
1
Country of publishing house
GB - UNITED KINGDOM
Number of pages
14
Pages from-to
470
UT code for WoS article
000955886200006
EID of the result in the Scopus database
2-s2.0-85147015685