Artificial intelligence‐based analysis of body composition in Marfan: skeletal muscle density and psoas muscle index predict aortic enlargement
Abstract Background Patients with Marfan syndrome are at risk for aortic enlargement and are routinely monitored by computed tomography (CT) imaging. The purpose of this study is to analyse body composition using artificial intelligence (AI)‐based tissue segmentation in patients with Marfan syndrome...
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Format: | Article |
Language: | English |
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Wiley
2021-08-01
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Series: | Journal of Cachexia, Sarcopenia and Muscle |
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Online Access: | https://doi.org/10.1002/jcsm.12731 |
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record_format |
Article |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Nick Lasse Beetz Christoph Maier Seyd Shnayien Tobias Daniel Trippel Petra Gehle Uli Fehrenbach Dominik Geisel |
spellingShingle |
Nick Lasse Beetz Christoph Maier Seyd Shnayien Tobias Daniel Trippel Petra Gehle Uli Fehrenbach Dominik Geisel Artificial intelligence‐based analysis of body composition in Marfan: skeletal muscle density and psoas muscle index predict aortic enlargement Journal of Cachexia, Sarcopenia and Muscle Marfan syndrome Aortic enlargement Body composition Sarcopenia |
author_facet |
Nick Lasse Beetz Christoph Maier Seyd Shnayien Tobias Daniel Trippel Petra Gehle Uli Fehrenbach Dominik Geisel |
author_sort |
Nick Lasse Beetz |
title |
Artificial intelligence‐based analysis of body composition in Marfan: skeletal muscle density and psoas muscle index predict aortic enlargement |
title_short |
Artificial intelligence‐based analysis of body composition in Marfan: skeletal muscle density and psoas muscle index predict aortic enlargement |
title_full |
Artificial intelligence‐based analysis of body composition in Marfan: skeletal muscle density and psoas muscle index predict aortic enlargement |
title_fullStr |
Artificial intelligence‐based analysis of body composition in Marfan: skeletal muscle density and psoas muscle index predict aortic enlargement |
title_full_unstemmed |
Artificial intelligence‐based analysis of body composition in Marfan: skeletal muscle density and psoas muscle index predict aortic enlargement |
title_sort |
artificial intelligence‐based analysis of body composition in marfan: skeletal muscle density and psoas muscle index predict aortic enlargement |
publisher |
Wiley |
series |
Journal of Cachexia, Sarcopenia and Muscle |
issn |
2190-5991 2190-6009 |
publishDate |
2021-08-01 |
description |
Abstract Background Patients with Marfan syndrome are at risk for aortic enlargement and are routinely monitored by computed tomography (CT) imaging. The purpose of this study is to analyse body composition using artificial intelligence (AI)‐based tissue segmentation in patients with Marfan syndrome in order to identify possible predictors of progressive aortic enlargement. Methods In this study, the body composition of 25 patients aged ≤50 years with Marfan syndrome and no prior aortic repair was analysed at the third lumbar vertebra (L3) level from a retrospective dataset using an AI‐based software tool (Visage Imaging). All patients underwent electrocardiography‐triggered CT of the aorta twice within 2 years for suspected progression of aortic disease, suspected dissection, and/or pre‐operative evaluation. Progression of aortic enlargement was defined as an increase in diameter at the aortic sinus or the ascending aorta of at least 2 mm. Patients meeting this definition were assigned to the ‘progressive aortic enlargement’ group (proAE group) and patients with stable diameters to the ‘stable aortic enlargement’ group (staAE group). Statistical analysis was performed using the Mann–Whitney U test. Two possible body composition predictors of aortic enlargement—skeletal muscle density (SMD) and psoas muscle index (PMI)—were analysed further using multivariant logistic regression analysis. Aortic enlargement was defined as the dependent variant, whereas PMI, SMD, age, sex, body mass index (BMI), beta blocker medication, and time interval between CT scans were defined as independent variants. Results There were 13 patients in the proAE group and 12 patients in the staAE group. AI‐based automated analysis of body composition at L3 revealed a significantly increased SMD measured in Hounsfield units (HUs) in patients with aortic enlargement (proAE group: 50.0 ± 8.6 HU vs. staAE group: 39.0 ± 15.0 HU; P = 0.03). PMI also trended towards higher values in the proAE group (proAE group: 6.8 ± 2.3 vs. staAE group: 5.6 ± 1.3; P = 0.19). Multivariate logistic regression revealed significant prediction of aortic enlargement for SMD (P = 0.05) and PMI (P = 0.04). Conclusions Artificial intelligence‐based analysis of body composition at L3 in Marfan patients is feasible and easily available from CT angiography. Analysis of body composition at L3 revealed significantly higher SMD in patients with progressive aortic enlargement. PMI and SMD significantly predicted aortic enlargement in these patients. Using body composition as a predictor of progressive aortic enlargement may contribute information for risk stratification regarding follow‐up intervals and the need for aortic repair. |
topic |
Marfan syndrome Aortic enlargement Body composition Sarcopenia |
url |
https://doi.org/10.1002/jcsm.12731 |
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doaj-edf62bbb519c4282962752fe9348b4a12021-08-09T05:46:55ZengWileyJournal of Cachexia, Sarcopenia and Muscle2190-59912190-60092021-08-0112499399910.1002/jcsm.12731Artificial intelligence‐based analysis of body composition in Marfan: skeletal muscle density and psoas muscle index predict aortic enlargementNick Lasse Beetz0Christoph Maier1Seyd Shnayien2Tobias Daniel Trippel3Petra Gehle4Uli Fehrenbach5Dominik Geisel6Charité—Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt‐Universität zu Berlin, Department of Radiology Augustenburger Platz 1 13353 Berlin GermanyCharité—Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt‐Universität zu Berlin, Department of Radiology Augustenburger Platz 1 13353 Berlin GermanyCharité—Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt‐Universität zu Berlin, Department of Radiology Hindenburgdamm 30 12203 Berlin GermanyCharité—Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt‐Universität zu Berlin, Department of Internal Medicine – Cardiology Berlin GermanyCharité—Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt‐Universität zu Berlin, Department of Internal Medicine – Cardiology Berlin GermanyCharité—Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt‐Universität zu Berlin, Department of Radiology Augustenburger Platz 1 13353 Berlin GermanyCharité—Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt‐Universität zu Berlin, Department of Radiology Augustenburger Platz 1 13353 Berlin GermanyAbstract Background Patients with Marfan syndrome are at risk for aortic enlargement and are routinely monitored by computed tomography (CT) imaging. The purpose of this study is to analyse body composition using artificial intelligence (AI)‐based tissue segmentation in patients with Marfan syndrome in order to identify possible predictors of progressive aortic enlargement. Methods In this study, the body composition of 25 patients aged ≤50 years with Marfan syndrome and no prior aortic repair was analysed at the third lumbar vertebra (L3) level from a retrospective dataset using an AI‐based software tool (Visage Imaging). All patients underwent electrocardiography‐triggered CT of the aorta twice within 2 years for suspected progression of aortic disease, suspected dissection, and/or pre‐operative evaluation. Progression of aortic enlargement was defined as an increase in diameter at the aortic sinus or the ascending aorta of at least 2 mm. Patients meeting this definition were assigned to the ‘progressive aortic enlargement’ group (proAE group) and patients with stable diameters to the ‘stable aortic enlargement’ group (staAE group). Statistical analysis was performed using the Mann–Whitney U test. Two possible body composition predictors of aortic enlargement—skeletal muscle density (SMD) and psoas muscle index (PMI)—were analysed further using multivariant logistic regression analysis. Aortic enlargement was defined as the dependent variant, whereas PMI, SMD, age, sex, body mass index (BMI), beta blocker medication, and time interval between CT scans were defined as independent variants. Results There were 13 patients in the proAE group and 12 patients in the staAE group. AI‐based automated analysis of body composition at L3 revealed a significantly increased SMD measured in Hounsfield units (HUs) in patients with aortic enlargement (proAE group: 50.0 ± 8.6 HU vs. staAE group: 39.0 ± 15.0 HU; P = 0.03). PMI also trended towards higher values in the proAE group (proAE group: 6.8 ± 2.3 vs. staAE group: 5.6 ± 1.3; P = 0.19). Multivariate logistic regression revealed significant prediction of aortic enlargement for SMD (P = 0.05) and PMI (P = 0.04). Conclusions Artificial intelligence‐based analysis of body composition at L3 in Marfan patients is feasible and easily available from CT angiography. Analysis of body composition at L3 revealed significantly higher SMD in patients with progressive aortic enlargement. PMI and SMD significantly predicted aortic enlargement in these patients. Using body composition as a predictor of progressive aortic enlargement may contribute information for risk stratification regarding follow‐up intervals and the need for aortic repair.https://doi.org/10.1002/jcsm.12731Marfan syndromeAortic enlargementBody compositionSarcopenia |