Department of Stroke Center, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China.
Front Endocrinol (Lausanne). 2024 Apr 19;15:1357580. doi: 10.3389/fendo.2024.1357580. eCollection 2024.
Type 2 Diabetes Mellitus (T2DM) with insulin resistance (IR) is prone to damage the vascular endothelial, leading to the formation of vulnerable carotid plaques and increasing ischemic stroke (IS) risk. The purpose of this study is to develop a nomogram model based on carotid ultrasound radiomics for predicting IS risk in T2DM patients.
198 T2DM patients were enrolled and separated into study and control groups based on IS history. After manually delineating carotid plaque region of interest (ROI) from images, radiomics features were identified and selected using the least absolute shrinkage and selection operator (LASSO) regression to calculate the radiomics score (RS). A combinatorial logistic machine learning model and nomograms were created using RS and clinical features like the triglyceride-glucose index. The three models were assessed using area under curve (AUC) and decision curve analysis (DCA).
Patients were divided into the training set and the testing set by the ratio of 0.7. 4 radiomics features were selected. RS and clinical variables were all statically significant in the training set and were used to create a combination model and a prediction nomogram. The combination model (radiomics + clinical nomogram) had the largest AUC in both the training set and the testing set (0.898 and 0.857), and DCA analysis showed that it had a higher overall net benefit compared to the other models.
This study created a carotid ultrasound radiomics machine-learning-based IS risk nomogram for T2DM patients with carotid plaques. Its diagnostic performance and clinical prediction capabilities enable accurate, convenient, and customized medical care.
2 型糖尿病（T2DM）合并胰岛素抵抗（IR）易损伤血管内皮，导致易损颈动脉斑块形成，增加缺血性脑卒中（IS）风险。本研究旨在建立基于颈动脉超声放射组学的列线图模型，预测 T2DM 患者 IS 风险。
纳入 198 例 T2DM 患者，根据 IS 史分为研究组和对照组。手动勾画颈动脉斑块感兴趣区（ROI）后，采用最小绝对值收缩和选择算子（LASSO）回归筛选放射组学特征并计算放射组学评分（RS）。采用 RS 及甘油三酯-葡萄糖指数等临床特征构建组合逻辑机器学习模型和列线图。采用曲线下面积（AUC）和决策曲线分析（DCA）评估 3 种模型。
患者按 0.7 的比例分为训练集和测试集。筛选出 4 个放射组学特征。RS 和临床变量在训练集和测试集均有统计学意义，用于构建组合模型和预测列线图。组合模型（放射组学+临床列线图）在训练集和测试集的 AUC 最大（0.898 和 0.857），DCA 分析显示其与其他模型相比具有更高的整体净获益。
本研究建立了基于颈动脉超声放射组学的 T2DM 合并颈动脉斑块患者 IS 风险列线图，其诊断性能和临床预测能力可实现精准、便捷、个体化的医疗服务。
