Kim Hyun Ji, Lee Heon, Lee Bora, Lee Jae Wook, Shin Kyung Eun, Suh Jon, Park Hyun Woo, Kim Jeong A
Department of Radiology, Wonmi-Gu, Bucheon.
Graduate School of Statistics, Chung-Ang University, Dongjak-gu, Seoul.
Medicine (Baltimore). 2019 Feb;98(7):e14601. doi: 10.1097/MD.0000000000014601.
There has been a marked increase in the use of low-dose computed tomography (LDCT) for lung cancer screening. However, the potential of LDCT to predict metabolic syndrome (MetS) has not been well-documented in this risk-sharing population. We assessed the reliability of epicardial fat volume (EFV) and epicardial fat area (EFA) measurements on chest LDCT for prediction of MetS.A total of 130 (mean age, 50.2 ± 10.77 years) asymptomatic male who underwent nonelectrocardiography (ECG)-gated LDCT were divided into 2 groups for the main analysis (n = 75) and validation (n = 55). Each group was further divided into subgroups with or without MetS. EFV and EFA were calculated semiautomatically using commercially available software with manual assistance. The area under the curve (AUC) on receiver operating characteristic (ROC) analysis and cutoff values to predict MetS on LDCT were then calculated and validated. Female data were not available for analysis due to small sample size in this self-referred lung cancer screening program.In the analysis group, the mean EFV was 123.12 ± 42.29 and 67.30 ± 20.68 cm for the MetS and non-MetS subgroups, respectively (P < .001), and the mean EFA was 7.95 ± 3.10 and 4.04 ± 1.73 cm, respectively (P < .001). Using 93.65 and 4.94 as the cutoffs for EFV and EFA, respectively, the sensitivity, specificity, positive and negative predictive values, and accuracy for predicting MetS were 84.2% and 84.2%, and 92.9% and 64.3% (P < .001); 80% and 44.4% (P = .01); 94.5% and 92.3%; and 90.7% and 69.3% (P < .001), respectively. The AUC for EFV and EFA for predicting MetS was 0.909 and 0.808 (95% confidence interval, 0.819-1.000 and 0.702-0.914, respectively) (P = .02). Using the same cutoff values in the analysis group, there was no significant difference in diagnostic performance using EFV and EFA between the analysis and validation sets.Although quantification of both EFA and EFV is feasible on non-ECG-gated LDCT, EFV may be used to reliably predict MetS with fairly high and better diagnostic performance in selected population.
低剂量计算机断层扫描(LDCT)在肺癌筛查中的应用显著增加。然而,在这个风险共享人群中,LDCT预测代谢综合征(MetS)的潜力尚未得到充分记录。我们评估了胸部LDCT上的心外膜脂肪体积(EFV)和心外膜脂肪面积(EFA)测量值对预测MetS的可靠性。共有130名(平均年龄50.2±10.77岁)无症状男性接受了非心电图(ECG)门控LDCT检查,分为主要分析组(n = 75)和验证组(n = 55)。每组再进一步分为有或无MetS的亚组。使用市售软件并辅以人工操作半自动计算EFV和EFA。然后计算并验证了受试者操作特征(ROC)分析的曲线下面积(AUC)以及LDCT上预测MetS的临界值。由于在这个自我推荐的肺癌筛查项目中女性样本量较小,因此无法获得女性数据进行分析。
在分析组中,MetS亚组和非MetS亚组的平均EFV分别为123.12±42.29和67.30±20.68 cm(P <.001),平均EFA分别为7.95±3.10和4.04±1.73 cm(P <.001)。分别以93.65和4.94作为EFV和EFA的临界值,预测MetS的敏感性、特异性、阳性和阴性预测值以及准确性分别为84.2%和84.2%,92.9%和64.3%(P <.001);80%和44.4%(P =.01);94.5%和92.3%;90.7%和69.3%(P <.001)。预测MetS的EFV和EFA的AUC分别为0.909和0.808(95%置信区间分别为0.819 - 1.000和0.702 - 0.914)(P =.02)。在分析组中使用相同的临界值,分析集和验证集之间使用EFV和EFA的诊断性能没有显著差异。
虽然在非ECG门控LDCT上对EFA和EFV进行量化都是可行的,但在特定人群中,EFV可能用于可靠地预测MetS,具有相当高且更好的诊断性能。