Department of Radiology, Fourth Affiliated Hospital of Harbin Medical University, No. 37, YiYuan Street, NanGang District, Harbin, 150001, Heilongjiang, China.
Department of Interventional and Vascular, Fourth Affiliated Hospital of Harbin Medical University, No. 37, YiYuan Street, NanGang District, Harbin, 150001, Heilongjiang, China.
Cardiovasc Diabetol. 2023 Jan 23;22(1):14. doi: 10.1186/s12933-023-01748-0.
Patients with type 2 diabetes mellitus (T2DM) are highly susceptible to cardiovascular disease, and coronary artery disease (CAD) is their leading cause of death. We aimed to assess whether computed tomography (CT) based imaging parameters and radiomic features of pericoronary adipose tissue (PCAT) can improve the diagnostic efficacy of whether patients with T2DM have developed CAD.
We retrospectively recruited 229 patients with T2DM but no CAD history (146 were diagnosed with CAD at this visit and 83 were not). We collected clinical information and extracted imaging manifestations from CT images and 93 radiomic features of PCAT from all patients. All patients were randomly divided into training and test groups at a ratio of 7:3. Four models were constructed, encapsulating clinical factors (Model 1), clinical factors and imaging indices (Model 2), clinical factors and Radscore (Model 3), and all together (Model 4), to identify patients with CAD. Receiver operating characteristic curves and decision curve analysis were plotted to evaluate the model performance and pairwise model comparisons were performed via the DeLong test to demonstrate the additive value of different factors.
In the test set, the areas under the curve (AUCs) of Model 2 and Model 4 were 0.930 and 0.929, respectively, with higher recognition effectiveness compared to the other two models (each p < 0.001). Of these models, Model 2 had higher diagnostic efficacy for CAD than Model 1 (p < 0.001, 95% CI [0.129-0.350]). However, Model 4 did not improve the effectiveness of the identification of CAD compared to Model 2 (p = 0.776); similarly, the AUC did not significantly differ between Model 3 (AUC = 0.693) and Model 1 (AUC = 0.691, p = 0.382). Overall, Model 2 was rated better for the diagnosis of CAD in patients with T2DM.
A comprehensive diagnostic model combining patient clinical risk factors with CT-based imaging parameters has superior efficacy in diagnosing the occurrence of CAD in patients with T2DM.
2 型糖尿病（T2DM）患者极易发生心血管疾病，而冠心病（CAD）是其主要死亡原因。本研究旨在评估基于计算机断层扫描（CT）的影像参数和冠状动脉脂肪组织（PCAT）的放射组学特征是否能提高诊断 T2DM 患者是否发生 CAD 的效能。
我们回顾性招募了 229 例无 CAD 病史的 T2DM 患者（本次就诊时 146 例被诊断为 CAD，83 例未被诊断为 CAD）。我们收集了临床信息，并从 CT 图像中提取影像表现和所有患者的 93 个 PCAT 放射组学特征。所有患者均以 7:3 的比例随机分为训练组和测试组。构建了 4 种模型，分别纳入临床因素（模型 1）、临床因素和影像学指标（模型 2）、临床因素和 Radscore（模型 3）以及所有因素（模型 4），以识别 CAD 患者。绘制受试者工作特征曲线和决策曲线分析，通过 DeLong 检验进行两两模型比较，以证明不同因素的附加价值。
在测试集中，模型 2 和模型 4 的曲线下面积（AUC）分别为 0.930 和 0.929，与其他两种模型相比，识别效果更高（均 P<0.001）。在这些模型中，模型 2 对 CAD 的诊断效能高于模型 1（P<0.001，95%CI[0.129-0.350]）。然而，与模型 2 相比，模型 4 并未提高 CAD 识别的有效性（P=0.776）；同样，模型 3（AUC=0.693）与模型 1（AUC=0.691，P=0.382）之间的 AUC 差异无统计学意义。总的来说，模型 2 更适合诊断 T2DM 患者的 CAD。
综合考虑患者临床风险因素和 CT 成像参数的诊断模型，在诊断 T2DM 患者 CAD 发生方面具有更高的效能。
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