Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Radiology, Augustenburger Platz 1, 13353, Berlin, Germany.
Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Radiology, Hindenburgdamm 30, 12203, Berlin, Germany.
J Cachexia Sarcopenia Muscle. 2021 Aug;12(4):993-999. doi: 10.1002/jcsm.12731. Epub 2021 Jun 17.
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.
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.
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).
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.
马凡综合征患者存在主动脉扩张的风险，通常通过计算机断层扫描（CT）成像进行监测。本研究旨在使用基于人工智能（AI）的组织分割分析马凡综合征患者的身体成分，以确定主动脉进行性扩张的可能预测因素。
在这项研究中，对 25 名年龄≤50 岁的马凡综合征患者的身体成分进行了分析，这些患者在第三次腰椎（L3）水平的回顾性数据集上使用基于 AI 的软件工具（Visage Imaging）进行了分析。所有患者在 2 年内两次因疑似主动脉疾病进展、疑似夹层和/或术前评估进行心电图触发的主动脉 CT 检查。主动脉扩张的进展定义为主动脉窦或升主动脉直径增加至少 2mm。符合该定义的患者被分配到“进行性主动脉扩张”组（proAE 组），而直径稳定的患者被分配到“稳定主动脉扩张”组（staAE 组）。使用曼-惠特尼 U 检验进行统计分析。进一步使用多变量逻辑回归分析对两种可能的主动脉扩张的身体成分预测因子-骨骼肌密度（SMD）和腰大肌指数（PMI）进行了分析。主动脉扩张被定义为依赖变量，而 PMI、SMD、年龄、性别、体重指数（BMI）、β受体阻滞剂药物和 CT 扫描之间的时间间隔被定义为独立变量。
在 proAE 组中有 13 名患者，在 staAE 组中有 12 名患者。在 L3 处基于 AI 的身体成分自动分析显示，主动脉扩张患者的 SMD（以亨氏单位[HU]测量）显著增加（proAE 组：50.0±8.6 HU vs. staAE 组：39.0±15.0 HU；P=0.03）。PMI 在 proAE 组中也呈上升趋势（proAE 组：6.8±2.3 vs. staAE 组：5.6±1.3；P=0.19）。多变量逻辑回归显示 SMD（P=0.05）和 PMI（P=0.04）对主动脉扩张有显著预测作用。
基于人工智能的马凡综合征患者 L3 处身体成分分析是可行的，并且可以从 CT 血管造影轻松获得。L3 处的身体成分分析显示，进行性主动脉扩张患者的 SMD 明显更高。PMI 和 SMD 显著预测了这些患者的主动脉扩张。使用身体成分作为进行性主动脉扩张的预测因子，可能有助于提供关于随访间隔和主动脉修复需求的风险分层信息。
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