Chen Qiaofei, Li Hongwei, Xie Wei, Abudukeremu Ayiguli, Wen Kexin, Liu Wenhao, Mai Jingting, Xu Xiaolin, Zhang Yuling
Department of Cardiology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, No. 107, West Yanjiang Road, Guangzhou, 510120, China.
Department of Cardiology, Guangzhou First People's Hospital, South China University of Technology, Guangzhou, China.
Int J Cardiovasc Imaging. 2025 Jan;41(1):123-136. doi: 10.1007/s10554-024-03296-4. Epub 2024 Nov 30.
Carotid plaque radiomics-included models have increased the predictive capacity of cardiovascular risk, but the radiomic features of these models were inconsistent in previous studies. Lipids could be used to select the most important radiomic feature. A retrospective case‒control study was performed in 153 diabetic and 76 non-diabetic patients with carotid plaque detected by ultrasound. Cerebro-cardiovascular disease (CCD), comprising coronary heart disease (CHD) and stroke, was the primary outcome. Clinical variables and radiomic features of longitudinal carotid plaque images were collected. Principal component analyses were used to compare the power of radiomic and lipid features in discrimination between diabetes, CCD patients, and their opposites. Partial least square regression, logistic regression analyses, and least absolute shrinkage and selection operator (LASSO) regression were performed for high-risk radiomic features. The diagnostic capacity of the models was evaluated. PCA based on radiomics or lipids did not show good discrimination of diabetes, CCD, and their opposites. There were 6 overlapping radiomic features associated with lipid profiles, but only original_firstorder_Mean was negatively associated with diabetic stroke [adjusted OR = 0.468 (0.243-0.902), P = 0.023] and nondiabetic CHD [adjusted OR = 0.311 (0.123-0.783), P = 0.013]. The associations remained independent in the LASSO regression models (β=-0.032 for diabetic stroke, and - 0.026 for non-diabetic CHD). The diagnostic capacity of lipid-related radiomics for diabetic stroke (0.556 to 0.688) and non-diabetic CHD (0.690 to 0.783) was increased by the combination of these clinical variables. Carotid plaque radiomics is associated with lipids and stroke in diabetes, and quantitative features are useful for therapeutic guidance and cardiovascular risk evaluation in clinical use.
包含颈动脉斑块影像组学的模型提高了心血管风险的预测能力，但这些模型的影像组学特征在以往研究中并不一致。脂质可用于选择最重要的影像组学特征。对153例经超声检测发现有颈动脉斑块的糖尿病患者和76例非糖尿病患者进行了一项回顾性病例对照研究。以包括冠心病（CHD）和中风在内的心脑血管疾病（CCD）作为主要结局。收集了颈动脉斑块纵向图像的临床变量和影像组学特征。采用主成分分析比较影像组学特征和脂质特征在区分糖尿病患者、CCD患者及其对照组方面的能力。对高危影像组学特征进行偏最小二乘回归、逻辑回归分析和最小绝对收缩和选择算子（LASSO）回归。评估模型的诊断能力。基于影像组学或脂质的主成分分析对糖尿病、CCD及其对照组的区分效果不佳。有6个与脂质谱相关的重叠影像组学特征，但只有original_firstorder_Mean与糖尿病性中风呈负相关[调整后的比值比（OR）=0.468（0.243 - 0.902），P = 0.023]，与非糖尿病性冠心病呈负相关[调整后的OR = 0.311（0.123 - 0.783），P = 0.013]。在LASSO回归模型中，这些关联仍然独立（糖尿病性中风的β=-0.032，非糖尿病性冠心病的β=-0.026）。这些临床变量的组合提高了脂质相关影像组学对糖尿病性中风（0.556至0.688）和非糖尿病性冠心病（0.690至0.783）的诊断能力。颈动脉斑块影像组学与糖尿病中的脂质和中风相关，定量特征在临床应用中对治疗指导和心血管风险评估有用。
