Imaging Markers Derived From MRI-Based Automated Kidney Segmentation—an Analysis of Data From the German National Cohort (NAKO Gesundheitsstudie).

Autor:	Kellner E; Division of Medical Physics, Department of Radiology, University Medical Center Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany; Institute of Genetic Epidemiology, Faculty of Medicine and Medical Center - University of Freiburg, Freiburg, Germany; Division of Medical Physics, Department of Diagnostic and Interventional Radiology, University Medical Center Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany; Institute of Genetic Epidemiology, Faculty of Medicine and Medical Center - University of Freiburg, Freiburg, Germany; Department of Diagnostic and Interventional Radiology, University Medical Center Freiburg, Faculty of Medicine, Albert-Ludwigs-University Freiburg, Germany; Institute of Clinical Chemistry and Laboratory Medicine, University Medicine Greifswald, Germany; DZHK (German Centre for Cardiovascular Research), Partner Site Greifswald, University Medicine Greifswald, Germany; Institute for Community Medicine, University Medicine Greifswald, Germany; Department of Diagnostic and Interventional Radiology, University Hospital Augsburg, Germany; Centre for Advanced Analytics and Predictive Sciences (CAAPS), University of Augsburg, Germany; Department of Diagnostical and Interventional Radiology, University Hospital Heidelberg, Germany; Institute of Social Medicine, Epidemiology and Health Economics, Charité - Universitätsmedizin Berlin, Institute of Clinical Epidemiology and Biometry, University of Würzburg, State Institute of Health I, Bavarian Health and Food Safety Authority, Erlangen, Germany; Max-Delbrueck-Center for Molecular Medicine in the Helmholtz Association (MDC), Molecular Epidemiology Research Group; Max-Delbrueck-Center for Molecular Medicine in the Helmholtz Association (MDC), Biobank Technology Platform, Berlin; Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Germany; Chair of Genetic Epidemiology, University of Regensburg, Germany; Institute of Epidemiology, Helmholtz Center Munich, German Research Center for Environmental Health, Neuherberg; Chair of Epidemiology, Institute for Medical Information Processing, Biometrics, and Epidemiology, Medical Faculty, Ludwig-Maximilians-University Munich; DZHK (German Centre for Cardiovascular Research), Partner Site Munich, Munich Heart Alliance, Munich; DZD (German Centre for Diabetes Research), Neuherberg; Berlin Ultrahigh Field Facility (B.U.F.F.), Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin; Institute of Epidemiology, Kiel University, Kiel, Germany; Department of Diagnostic and Interventional Radiology, Core Facility MRDAC, University Medical Center Freiburg, Faculty of Medicine, Albert-Ludwigs-University Freiburg, Germany., Sekula P, Lipovsek J, Russe M, Horbach H, Schlett CL, Nauck M, Völzke H, Kroencke T, Bette S, Kauczor HU, Keil T, Pischon T, Heid IM, Peters A, Niendorf T, Lieb W, Bamberg F, Büchert M, Reichardt W, Reisert M, Köttgen A
Jazyk:	angličtina
Zdroj:	Deutsches Arzteblatt international [Dtsch Arztebl Int] 2024 May 03; Vol. 121 (9), pp. 284-290.
DOI:	10.3238/arztebl.m2024.0040
Abstrakt:	Background: Population-wide research on potential new imaging biomarkers of the kidney depends on accurate automated segmentation of the kidney and its compartments (cortex, medulla, and sinus). Methods: We developed a robust deep-learning framework for kidney (sub-)segmentation based on a hierarchical, three-dimensional convolutional neural network (CNN) that was optimized for multiscale problems of combined localization and segmentation. We applied the CNN to abdominal magnetic resonance images from the population-based German National Cohort (NAKO) study. Results: There was good to excellent agreement between the model predictions and manual segmentations. The median values for the body-surface normalized total kidney, cortex, medulla, and sinus volumes of 9934 persons were 158, 115, 43, and 24 mL/m2. Distributions of these markers are provided both for the overall study population and for a subgroup of persons without kidney disease or any associated conditions. Multivariable adjusted regression analyses revealed that diabetes, male sex, and a higher estimated glomerular filtration rate (eGFR) are important predictors of higher total and cortical volumes. Each increase of eGFR by one unit (i.e., 1 mL/min per 1.73 m2 body surface area) was associated with a 0.98 mL/m2 increase in total kidney volume, and this association was significant. Volumes were lower in persons with eGFR-defined chronic kidney disease. Conclusion: The extraction of image-based biomarkers through CNN-based renal sub-segmentation using data from a population-based study yields reliable results, forming a solid foundation for future investigations.
Databáze:	MEDLINE
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