Deep learning-based body composition analysis from whole-body magnetic resonance imaging to predict all-cause mortality in a large western populationResearch in context
Summary: Background: Manually extracted imaging-based body composition measures from a single-slice area (A) have shown associations with clinical outcomes in patients with cardiometabolic disease and cancer. With advances in artificial intelligence, fully automated volumetric (V) segmentation appr...
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Elsevier
2024-12-01
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352396424005036 |
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| author | Matthias Jung Vineet K. Raghu Marco Reisert Hanna Rieder Susanne Rospleszcz Tobias Pischon Thoralf Niendorf Hans-Ulrich Kauczor Henry Völzke Robin Bülow Maximilian F. Russe Christopher L. Schlett Michael T. Lu Fabian Bamberg Jakob Weiss |
| author_facet | Matthias Jung Vineet K. Raghu Marco Reisert Hanna Rieder Susanne Rospleszcz Tobias Pischon Thoralf Niendorf Hans-Ulrich Kauczor Henry Völzke Robin Bülow Maximilian F. Russe Christopher L. Schlett Michael T. Lu Fabian Bamberg Jakob Weiss |
| author_sort | Matthias Jung |
| collection | DOAJ |
| description | Summary: Background: Manually extracted imaging-based body composition measures from a single-slice area (A) have shown associations with clinical outcomes in patients with cardiometabolic disease and cancer. With advances in artificial intelligence, fully automated volumetric (V) segmentation approaches are now possible, but it is unknown whether these measures carry prognostic value to predict mortality in the general population. Here, we developed and tested a deep learning framework to automatically quantify volumetric body composition measures from whole-body magnetic resonance imaging (MRI) and investigated their prognostic value to predict mortality in a large Western population. Methods: The framework was developed using data from two large Western European population-based cohort studies, the UK Biobank (UKBB) and the German National Cohort (NAKO). Body composition was defined as (i) subcutaneous adipose tissue (SAT), (ii) visceral adipose tissue (VAT), (iii) skeletal muscle (SM), SM fat fraction (SMFF), and (iv) intramuscular adipose tissue (IMAT). The prognostic value of the body composition measures was assessed in the UKBB using Cox regression analysis. Additionally, we extracted body composition areas for every level of the thoracic and lumbar spine (i) to compare the proposed volumetric whole-body approach to the currently established single-slice area approach on the height of the L3 vertebra and (ii) to investigate the correlation between volumetric and single slice area body composition measures on the level of each vertebral body. Findings: In 36,317 UKBB participants (mean age 65.1 ± 7.8 years, age range 45–84 years; 51.7% female; 1.7% [634/36,471] all-cause deaths; median follow-up 4.8 years), Cox regression revealed an independent association between VSM (adjusted hazard ratio [aHR]: 0.88, 95% confidence interval [CI] [0.81–0.91], p = 0.00023), VSMFF (aHR: 1.06, 95% CI [1.02–1.10], p = 0.0043), and VIMAT (aHR: 1.19, 95% CI [1.05–1.35], p = 0.0056) and mortality after adjustment for demographics (age, sex, BMI, race) and cardiometabolic risk factors (alcohol consumption, smoking status, hypertension, diabetes, history of cancer, blood serum markers). This association was attenuated when using traditional single-slice area measures. Highest correlation coefficients (R) between volumetric and single-slice area body composition measures were located at vertebra L5 for SAT (R = 0.820) and SMFF (R = 0.947), at L3 for VAT (R = 0.892), SM (R = 0.944), and at L4 for IMAT (R = 0.546) (all p < 0.0001). A similar pattern was found in 23,725 NAKO participants (mean age 53.9 ± 8.3 years, age range 40–75; 44.9% female). Interpretation: Automated volumetric body composition assessment from whole-body MRI predicted mortality in a large Western population beyond traditional clinical risk factors. Single slice areas were highly correlated with volumetric body composition measures but their association with mortality attenuated after multivariable adjustment. As volumetric body composition measures are increasingly accessible using automated techniques, identifying high-risk individuals may help to improve personalised prevention and lifestyle interventions. Funding: This project was conducted using data from the German National Cohort (NAKO) (www.nako.de). The NAKO is funded by the Federal Ministry of Education and Research (BMBF) [project funding reference numbers: 01ER1301A/B/C, 01ER1511D, and 01ER1801A/B/C/D], federal states of Germany and the Helmholtz Association, the participating universities and the institutes of the Leibniz Association. This research has been conducted using the UK Biobank Resource under Application Number 80337. MJ was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)-518480401. VKR was funded by American Heart Association Career Development Award 935176 and National Heart, Lung, and Blood Institute-K01HL168231. |
| format | Article |
| id | doaj-art-43facf6e0c7b467a8fc3e2f7338582bc |
| institution | DOAJ |
| issn | 2352-3964 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | EBioMedicine |
| spelling | doaj-art-43facf6e0c7b467a8fc3e2f7338582bc2025-08-20T02:51:14ZengElsevierEBioMedicine2352-39642024-12-0111010546710.1016/j.ebiom.2024.105467Deep learning-based body composition analysis from whole-body magnetic resonance imaging to predict all-cause mortality in a large western populationResearch in contextMatthias Jung0Vineet K. Raghu1Marco Reisert2Hanna Rieder3Susanne Rospleszcz4Tobias Pischon5Thoralf Niendorf6Hans-Ulrich Kauczor7Henry Völzke8Robin Bülow9Maximilian F. Russe10Christopher L. Schlett11Michael T. Lu12Fabian Bamberg13Jakob Weiss14Department of Diagnostic and Interventional Radiology, University Medical Center Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany; Department of Radiology, Cardiovascular Imaging Research Center, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA; Corresponding author. Massachusetts General Hospital, 165 Cambridge St, Suite 400, Boston, MA, 02114, USA; Department of Diagnostic and Interventional Radiology, University Medical Center Freiburg, Hugstetter Strasse 55, 79106 Freiburg, Germany.Department of Radiology, Cardiovascular Imaging Research Center, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USAMedical Physics, Department of Diagnostic and Interventional Radiology, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, 79106, Germany; Department of Stereotactic and Functional Neurosurgery, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, 79106, GermanyDepartment of Diagnostic and Interventional Radiology, University Medical Center Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, GermanyDepartment of Diagnostic and Interventional Radiology, University Medical Center Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany; Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, GermanyMolecular Epidemiology Research Group, Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin, 13125, GermanyBerlin Ultrahigh Field Facility, Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin, 13125, GermanyDepartment of Diagnostic and Interventional Radiology, Member of the German Center of Lung Research, University Hospital Heidelberg, Heidelberg, 69120, GermanyInstitute for Community Medicine, Ernst Moritz Arndt University, Greifswald, 17489, GermanyInstitute for Diagnostic Radiology and Neuroradiology, University Medicine, Ernst Moritz Arndt University Greifswald, Greifswald, 17475, GermanyDepartment of Diagnostic and Interventional Radiology, University Medical Center Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, GermanyDepartment of Diagnostic and Interventional Radiology, University Medical Center Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, GermanyDepartment of Radiology, Cardiovascular Imaging Research Center, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USADepartment of Diagnostic and Interventional Radiology, University Medical Center Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, GermanyDepartment of Diagnostic and Interventional Radiology, University Medical Center Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, GermanySummary: Background: Manually extracted imaging-based body composition measures from a single-slice area (A) have shown associations with clinical outcomes in patients with cardiometabolic disease and cancer. With advances in artificial intelligence, fully automated volumetric (V) segmentation approaches are now possible, but it is unknown whether these measures carry prognostic value to predict mortality in the general population. Here, we developed and tested a deep learning framework to automatically quantify volumetric body composition measures from whole-body magnetic resonance imaging (MRI) and investigated their prognostic value to predict mortality in a large Western population. Methods: The framework was developed using data from two large Western European population-based cohort studies, the UK Biobank (UKBB) and the German National Cohort (NAKO). Body composition was defined as (i) subcutaneous adipose tissue (SAT), (ii) visceral adipose tissue (VAT), (iii) skeletal muscle (SM), SM fat fraction (SMFF), and (iv) intramuscular adipose tissue (IMAT). The prognostic value of the body composition measures was assessed in the UKBB using Cox regression analysis. Additionally, we extracted body composition areas for every level of the thoracic and lumbar spine (i) to compare the proposed volumetric whole-body approach to the currently established single-slice area approach on the height of the L3 vertebra and (ii) to investigate the correlation between volumetric and single slice area body composition measures on the level of each vertebral body. Findings: In 36,317 UKBB participants (mean age 65.1 ± 7.8 years, age range 45–84 years; 51.7% female; 1.7% [634/36,471] all-cause deaths; median follow-up 4.8 years), Cox regression revealed an independent association between VSM (adjusted hazard ratio [aHR]: 0.88, 95% confidence interval [CI] [0.81–0.91], p = 0.00023), VSMFF (aHR: 1.06, 95% CI [1.02–1.10], p = 0.0043), and VIMAT (aHR: 1.19, 95% CI [1.05–1.35], p = 0.0056) and mortality after adjustment for demographics (age, sex, BMI, race) and cardiometabolic risk factors (alcohol consumption, smoking status, hypertension, diabetes, history of cancer, blood serum markers). This association was attenuated when using traditional single-slice area measures. Highest correlation coefficients (R) between volumetric and single-slice area body composition measures were located at vertebra L5 for SAT (R = 0.820) and SMFF (R = 0.947), at L3 for VAT (R = 0.892), SM (R = 0.944), and at L4 for IMAT (R = 0.546) (all p < 0.0001). A similar pattern was found in 23,725 NAKO participants (mean age 53.9 ± 8.3 years, age range 40–75; 44.9% female). Interpretation: Automated volumetric body composition assessment from whole-body MRI predicted mortality in a large Western population beyond traditional clinical risk factors. Single slice areas were highly correlated with volumetric body composition measures but their association with mortality attenuated after multivariable adjustment. As volumetric body composition measures are increasingly accessible using automated techniques, identifying high-risk individuals may help to improve personalised prevention and lifestyle interventions. Funding: This project was conducted using data from the German National Cohort (NAKO) (www.nako.de). The NAKO is funded by the Federal Ministry of Education and Research (BMBF) [project funding reference numbers: 01ER1301A/B/C, 01ER1511D, and 01ER1801A/B/C/D], federal states of Germany and the Helmholtz Association, the participating universities and the institutes of the Leibniz Association. This research has been conducted using the UK Biobank Resource under Application Number 80337. MJ was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)-518480401. VKR was funded by American Heart Association Career Development Award 935176 and National Heart, Lung, and Blood Institute-K01HL168231.http://www.sciencedirect.com/science/article/pii/S2352396424005036Magnetic resonance imagingArtificial intelligenceDeep learningBody compositionPublic healthMortality |
| spellingShingle | Matthias Jung Vineet K. Raghu Marco Reisert Hanna Rieder Susanne Rospleszcz Tobias Pischon Thoralf Niendorf Hans-Ulrich Kauczor Henry Völzke Robin Bülow Maximilian F. Russe Christopher L. Schlett Michael T. Lu Fabian Bamberg Jakob Weiss Deep learning-based body composition analysis from whole-body magnetic resonance imaging to predict all-cause mortality in a large western populationResearch in context EBioMedicine Magnetic resonance imaging Artificial intelligence Deep learning Body composition Public health Mortality |
| title | Deep learning-based body composition analysis from whole-body magnetic resonance imaging to predict all-cause mortality in a large western populationResearch in context |
| title_full | Deep learning-based body composition analysis from whole-body magnetic resonance imaging to predict all-cause mortality in a large western populationResearch in context |
| title_fullStr | Deep learning-based body composition analysis from whole-body magnetic resonance imaging to predict all-cause mortality in a large western populationResearch in context |
| title_full_unstemmed | Deep learning-based body composition analysis from whole-body magnetic resonance imaging to predict all-cause mortality in a large western populationResearch in context |
| title_short | Deep learning-based body composition analysis from whole-body magnetic resonance imaging to predict all-cause mortality in a large western populationResearch in context |
| title_sort | deep learning based body composition analysis from whole body magnetic resonance imaging to predict all cause mortality in a large western populationresearch in context |
| topic | Magnetic resonance imaging Artificial intelligence Deep learning Body composition Public health Mortality |
| url | http://www.sciencedirect.com/science/article/pii/S2352396424005036 |
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