Pooler B Dustin, Garrett John W, Lee Matthew H, Rush Benjamin E, Kuchnia Adam J, Summers Ronald M, Pickhardt Perry J
Department of Radiology, University of Wisconsin School of Medicine & Public Health, E3/311 Clinical Science Center, 600 Highland Ave, Madison, WI 53792-3252.
Department of Nutritional Sciences, College of Agricultural & Life Sciences, University of Wisconsin-Madison, Madison, WI.
AJR Am J Roentgenol. 2025 Mar;224(3):e2432216. doi: 10.2214/AJR.24.32216. Epub 2025 Jan 8.
CT-based abdominal body composition measures have shown associations with important health outcomes. Advances in artificial intelligence (AI) now allow deployment of tools that measure body composition in large patient populations. The purpose of this study was to assess associations of age, sex, and common systemic diseases with CT-based body composition measurements derived using a panel of fully automated AI tools in a population-level adult patient sample. This retrospective study included 140,606 adult patients (67,613 men and 72,993 women; mean age, 53.1 ± 17.6 [SD] years) who underwent abdominal CT at a single academic institution between January 1, 2000, and February 28, 2021. CT examinations were not restricted on the basis of patient setting, clinical indication, or IV contrast media use. Thirteen fully automated AI body composition tools quantifying liver, spleen, and kidney volume and attenuation; vertebral trabecular attenuation; skeletal muscle area and attenuation; and abdominal fat area and attenuation were applied to each patient's first available abdominal CT examination. EHR review was performed to identify common systemic diseases, including cancer, cardiovascular disease (CVD), diabetes mellitus (DM), and cirrhosis, on the basis of relevant ICD-10 codes; 64,789 patients (46.1%) had at least one systemic disease diagnosed. Multiple linear regression models were performed for the 118,141 patients (84.0%) with no systemic disease or a single systemic disease, to assess age, sex, and the presence of systemic disease as predictors of body composition measures; effect sizes were characterized using the unstandardized regression coefficient . Multiple linear regression models using age, sex, and systemic disease as predictors were overall significant for all 13 body composition measures (all < .001) with variable goodness of fit ( = 0.03-0.43 across models). In the models, age was predictive of all 13 body composition measures; sex, 12 measures; cancer, nine measures; CVD, 11 measures; DM, 13 measures; and cirrhosis, 12 measures (all < .05). Age, sex, and the presence of common systemic diseases were predictors of AI-derived CT-based body composition measures. An understanding of the identified associations with common systemic diseases will be critical for establishing normative reference ranges as CT-based AI body composition tools are developed for clinical use.
基于CT的腹部身体成分测量已显示出与重要健康结果之间的关联。人工智能（AI）的进步现在使得能够部署在大量患者群体中测量身体成分的工具。本研究的目的是在一个成年患者群体样本中，评估年龄、性别和常见全身性疾病与使用一组全自动AI工具得出的基于CT的身体成分测量值之间的关联。这项回顾性研究纳入了2000年1月1日至2021年2月28日期间在一家学术机构接受腹部CT检查的140,606名成年患者（67,613名男性和72,993名女性；平均年龄53.1±17.6[标准差]岁）。CT检查不受患者背景、临床指征或静脉造影剂使用情况的限制。13种全自动AI身体成分工具被应用于每位患者首次可获得的腹部CT检查，这些工具用于量化肝脏、脾脏和肾脏的体积及衰减；椎体小梁衰减；骨骼肌面积和衰减；以及腹部脂肪面积和衰减。通过电子健康记录（EHR）回顾，根据相关的国际疾病分类第十版（ICD - 10）编码来识别常见的全身性疾病，包括癌症、心血管疾病（CVD）、糖尿病（DM）和肝硬化；64,789名患者（46.1%）被诊断患有至少一种全身性疾病。对118,141名无全身性疾病或仅有一种全身性疾病的患者进行了多元线性回归模型分析，以评估年龄、性别和全身性疾病的存在作为身体成分测量指标的预测因素；效应大小使用非标准化回归系数来表征。以年龄、性别和全身性疾病作为预测因素的多元线性回归模型对于所有13种身体成分测量指标总体上均具有显著性（所有P值均<0.001），模型的拟合优度各不相同（各模型的R² = 0.03 - 0.43）。在这些模型中，年龄可预测所有13种身体成分测量指标；性别可预测12种；癌症可预测9种；CVD可预测11种；DM可预测13种；肝硬化可预测12种（所有P值均<0.05）。年龄、性别和常见全身性疾病的存在是基于AI的CT身体成分测量指标的预测因素。随着基于CT的AI身体成分工具被开发用于临床应用，了解所确定的与常见全身性疾病的关联对于建立规范参考范围至关重要。
