Deep Learning-Based 3D CT Body Composition Is Associated With Mortality in Patients Undergoing Transcatheter Aortic Valve Replacement.

IF 9.1 1区医学 Q1 CARDIAC & CARDIOVASCULAR SYSTEMS

Circulation: Cardiovascular Imaging Pub Date : 2026-10-20 DOI:10.1161/CIRCIMAGING.126.020106

Ibrahim Hadzic, Jan M Brendel, Thomas Mayrhofer, Matthias Jung, Lauren H Cooke, Marton Kolossvary, Evin Yucel, Sara Ersözlü, Vencel Juhasz, Isabel L Langenbach, Marcel C Langenbach, Nilay K Patel, Shady Abohashem, Vineet K Raghu, Brian Ghoshhajra, Sandeep Hedgire, Konstantin Nikolaou, Ahmed Tawakol, Michael T Lu, Pamela S Douglas, Hugo J W L Aerts, Borek Foldyna

{"title":"Deep Learning-Based 3D CT Body Composition Is Associated With Mortality in Patients Undergoing Transcatheter Aortic Valve Replacement.","authors":"Ibrahim Hadzic, Jan M Brendel, Thomas Mayrhofer, Matthias Jung, Lauren H Cooke, Marton Kolossvary, Evin Yucel, Sara Ersözlü, Vencel Juhasz, Isabel L Langenbach, Marcel C Langenbach, Nilay K Patel, Shady Abohashem, Vineet K Raghu, Brian Ghoshhajra, Sandeep Hedgire, Konstantin Nikolaou, Ahmed Tawakol, Michael T Lu, Pamela S Douglas, Hugo J W L Aerts, Borek Foldyna","doi":"10.1161/CIRCIMAGING.126.020106","DOIUrl":null,"url":null,"abstract":"Background: Recent advances in artificial intelligence enable fully automated 3-dimensional computed tomography (CT)-based body composition quantification. We investigated whether automated CT-derived thoraco-abdominopelvic body composition measures are associated with mortality after transcatheter aortic valve replacement.Methods: In this retrospective multicenter study, consecutive patients undergoing transcatheter aortic valve replacement with preprocedural CT imaging were included between 2014 and 2023. A deep learning-based pipeline performed automated 3-dimensional segmentation of thoraco-abdominopelvic noncontrast CT scans to quantify volumes (L/m2) and attenuations (Hounsfield units) of subcutaneous adipose tissue, visceral adipose tissue, muscular adipose tissue, and lean skeletal muscle, indexed to body surface area. Associations with mortality were evaluated using Cox models and Harrell C statistics, adjusting for demographics (age, sex, body mass index) and cardiometabolic risk factors (hypertension, diabetes, chronic renal failure).Results: Among 915 patients (median age, 84 years [interquartile range, 77-90]; 58% men), 160 (17.5%) died over a median follow-up of 22 months (interquartile range, 14-36). Restricted cubic spline analyses demonstrated that lower subcutaneous adipose tissue volume and higher adipose tissue attenuation were associated with increased mortality, whereas higher skeletal muscle volume and attenuation were associated with improved survival. Addition of body composition measures provided incremental discriminatory value beyond demographics and cardiometabolic risk factors (Harrell C, 0.66 versus 0.71; P<0.001).Conclusions: Automated 3-dimensional CT-derived body composition measures provide independent prognostic information and improve risk stratification for mortality after transcatheter aortic valve replacement. Low subcutaneous adipose tissue volume and higher adipose tissue attenuation identify patients at increased risk, whereas higher skeletal muscle volume and attenuation are associated with lower mortality.","PeriodicalId":10202,"journal":{"name":"Circulation: Cardiovascular Imaging","volume":" ","pages":"e020106"},"PeriodicalIF":9.1000,"publicationDate":"2026-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Circulation: Cardiovascular Imaging","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1161/CIRCIMAGING.126.020106","RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CARDIAC & CARDIOVASCULAR SYSTEMS","Score":null,"Total":0}

引用次数: 0

Abstract

Background: Recent advances in artificial intelligence enable fully automated 3-dimensional computed tomography (CT)-based body composition quantification. We investigated whether automated CT-derived thoraco-abdominopelvic body composition measures are associated with mortality after transcatheter aortic valve replacement.

Methods: In this retrospective multicenter study, consecutive patients undergoing transcatheter aortic valve replacement with preprocedural CT imaging were included between 2014 and 2023. A deep learning-based pipeline performed automated 3-dimensional segmentation of thoraco-abdominopelvic noncontrast CT scans to quantify volumes (L/m²) and attenuations (Hounsfield units) of subcutaneous adipose tissue, visceral adipose tissue, muscular adipose tissue, and lean skeletal muscle, indexed to body surface area. Associations with mortality were evaluated using Cox models and Harrell C statistics, adjusting for demographics (age, sex, body mass index) and cardiometabolic risk factors (hypertension, diabetes, chronic renal failure).

Results: Among 915 patients (median age, 84 years [interquartile range, 77-90]; 58% men), 160 (17.5%) died over a median follow-up of 22 months (interquartile range, 14-36). Restricted cubic spline analyses demonstrated that lower subcutaneous adipose tissue volume and higher adipose tissue attenuation were associated with increased mortality, whereas higher skeletal muscle volume and attenuation were associated with improved survival. Addition of body composition measures provided incremental discriminatory value beyond demographics and cardiometabolic risk factors (Harrell C, 0.66 versus 0.71; P<0.001).

Conclusions: Automated 3-dimensional CT-derived body composition measures provide independent prognostic information and improve risk stratification for mortality after transcatheter aortic valve replacement. Low subcutaneous adipose tissue volume and higher adipose tissue attenuation identify patients at increased risk, whereas higher skeletal muscle volume and attenuation are associated with lower mortality.

查看原文本刊更多论文

基于深度学习的3D CT身体组成与经导管主动脉瓣置换术患者的死亡率相关

背景：人工智能的最新进展使基于三维计算机断层扫描（CT）的全自动身体成分量化成为可能。我们调查了经导管主动脉瓣置换术后自动ct衍生的胸腹骨盆体成分测量是否与死亡率相关。方法：在这项回顾性多中心研究中，纳入了2014年至2023年间连续行经导管主动脉瓣置换术并术前CT成像的患者。基于深度学习的管道对胸腹骨盆非对比CT扫描进行自动三维分割，以量化皮下脂肪组织、内脏脂肪组织、肌肉脂肪组织和瘦骨骼肌的体积（L/m2）和衰减（Hounsfield单位），并以体表面积为索引。使用Cox模型和Harrell C统计评估与死亡率的关系，调整人口统计学（年龄、性别、体重指数）和心脏代谢危险因素（高血压、糖尿病、慢性肾衰竭）。结果：915例患者（中位年龄84岁[四分位数范围77-90]；58%为男性）中位随访22个月（四分位数范围14-36），160例（17.5%）死亡。限制性三次样条分析表明，较低的皮下脂肪组织体积和较高的脂肪组织衰减与死亡率增加有关，而较高的骨骼肌体积和衰减与生存率提高有关。除了人口统计学和心脏代谢危险因素外，身体成分测量的增加提供了增量的区别价值（Harrell C, 0.66 vs 0.71; p）结论：自动三维ct衍生的身体成分测量提供了独立的预后信息，并改善了经导管主动脉瓣置换术后死亡率的风险分层。较低的皮下脂肪组织体积和较高的脂肪组织衰减表明患者风险增加，而较高的骨骼肌体积和衰减与较低的死亡率相关。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Circulation: Cardiovascular Imaging 医学-核医学

CiteScore

6.30

自引率

2.70%

发文量

225

审稿时长

6-12 weeks

期刊介绍： Circulation: Cardiovascular Imaging, an American Heart Association journal, publishes high-quality, patient-centric articles focusing on observational studies, clinical trials, and advances in applied (translational) research. The journal features innovative, multimodality approaches to the diagnosis and risk stratification of cardiovascular disease. Modalities covered include echocardiography, cardiac computed tomography, cardiac magnetic resonance imaging and spectroscopy, magnetic resonance angiography, cardiac positron emission tomography, noninvasive assessment of vascular and endothelial function, radionuclide imaging, molecular imaging, and others. Article types considered by Circulation: Cardiovascular Imaging include Original Research, Research Letters, Advances in Cardiovascular Imaging, Clinical Implications of Molecular Imaging Research, How to Use Imaging, Translating Novel Imaging Technologies into Clinical Applications, and Cardiovascular Images.