Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, 19104, Philadelphia, PA, USA.
Department of Surgery, Seoul National University Hospital Healthcare System Gangnam Center, 06236, Seoul, South Korea.
J Transl Med. 2024 May 8;22(1):434. doi: 10.1186/s12967-024-05163-1.
Cardiometabolic disorders pose significant health risks globally. Metabolic syndrome, characterized by a cluster of potentially reversible metabolic abnormalities, is a known risk factor for these disorders. Early detection and intervention for individuals with metabolic abnormalities can help mitigate the risk of developing more serious cardiometabolic conditions. This study aimed to develop an image-derived phenotype (IDP) for metabolic abnormality from unenhanced abdominal computed tomography (CT) scans using deep learning. We used this IDP to classify individuals with metabolic syndrome and predict future occurrence of cardiometabolic disorders.
A multi-stage deep learning approach was used to extract the IDP from the liver region of unenhanced abdominal CT scans. In a cohort of over 2,000 individuals the IDP was used to classify individuals with metabolic syndrome. In a subset of over 1,300 individuals, the IDP was used to predict future occurrence of hypertension, type II diabetes, and fatty liver disease.
For metabolic syndrome (MetS) classification, we compared the performance of the proposed IDP to liver attenuation and visceral adipose tissue area (VAT). The proposed IDP showed the strongest performance (AUC 0.82) compared to attenuation (AUC 0.70) and VAT (AUC 0.80). For disease prediction, we compared the performance of the IDP to baseline MetS diagnosis. The models including the IDP outperformed MetS for type II diabetes (AUCs 0.91 and 0.90) and fatty liver disease (AUCs 0.67 and 0.62) prediction and performed comparably for hypertension prediction (AUCs of 0.77).
This study demonstrated the superior performance of a deep learning IDP compared to traditional radiomic features to classify individuals with metabolic syndrome. Additionally, the IDP outperformed the clinical definition of metabolic syndrome in predicting future morbidities. Our findings underscore the utility of data-driven imaging phenotypes as valuable tools in the assessment and management of metabolic syndrome and cardiometabolic disorders.
代谢紊乱会对全球健康造成严重威胁。代谢综合征是一组潜在可逆转代谢异常的特征，是这些疾病的已知危险因素。对代谢异常的个体进行早期检测和干预有助于降低发生更严重的心血管代谢疾病的风险。本研究旨在使用深度学习从未增强腹部 CT 扫描中开发代谢异常的图像衍生表型（IDP）。我们使用此 IDP 对代谢综合征患者进行分类，并预测未来心血管代谢疾病的发生。
使用多阶段深度学习方法从未增强腹部 CT 扫描的肝脏区域提取 IDP。在超过 2000 人的队列中，使用 IDP 对代谢综合征患者进行分类。在超过 1300 人的亚组中，使用 IDP 预测高血压、2 型糖尿病和脂肪肝的未来发生情况。
对于代谢综合征（MetS）分类，我们将所提出的 IDP 与肝脏衰减和内脏脂肪组织面积（VAT）的性能进行了比较。与衰减（AUC 0.70）和 VAT（AUC 0.80）相比，所提出的 IDP 显示出最强的性能（AUC 0.82）。对于疾病预测，我们将 IDP 的性能与基线 MetS 诊断进行了比较。包括 IDP 的模型在预测 2 型糖尿病（AUC 分别为 0.91 和 0.90）和脂肪肝（AUC 分别为 0.67 和 0.62）方面优于 MetS，在预测高血压方面表现相当（AUC 为 0.77）。
本研究表明，与传统的放射组学特征相比，深度学习 IDP 可更好地对代谢综合征患者进行分类。此外，IDP 在预测未来发病方面优于代谢综合征的临床定义。我们的研究结果强调了数据驱动的成像表型作为评估和管理代谢综合征和心血管代谢疾病的有价值工具的实用性。
