Combining morphological and biomechanical factors for optimal carotid plaque progression prediction: An MRI-based follow-up study using 3D thin-layer models
Combining morphological and biomechanical factors for optimal carotid plaque progression prediction: An MRI-based follow-up study using 3D thin-layer models
复制标题
结合形态学和生物力学因素进行最佳颈动脉斑块进展预测：使用 3D 薄层模型进行基于 MRI 的后续研究
DOI：
10.1016/j.ijcard.2019.07.005
复制
发表时间：
2019-10-15
影响因子：
3.5
通讯作者：
Yuan, Chun
中科院分区：
文献类型：
--
作者：
Wang, Qingyu;Tang, Dalin;Yuan, Chun
斑块进展预测对心血管研究和疾病的诊断、预防和治疗具有重要意义。获得20例患者颈动脉粥样硬化斑块的磁共振成像（MRI）数据。建立三维薄层模型，计算菌斑应力和应变。提取10个形态学和生物力学危险因素数据进行分析。选择壁厚增加（WTI）、斑块负担增加（PBI）和斑块面积增加（PAI）作为斑块进展的三个指标。采用5重交叉验证策略的广义线性混合模型（GLMM）计算预测精度并确定最优预测器。PBI的最佳预测的组合腔面积(LA)、斑块面积(PA),脂质百分比(LP)、壁厚(WT),最大斑块墙应力(”)和最大斑块壁应变与预测精度(MPWSn) = 1.4146(接受者操作特征曲线下面积(AUC)值是0.7158),而PA,斑块负担(PB)、WT, LP最小帽厚度、女性”和MPWSn是最好的WTI(精度= 1.3140,AUC = 0.6552)和PA、PB、WT,”,MPWSn和平均菌斑壁应变（APWSn）对PAI的预测精度为1.3025 （AUC = 0.6657）。组合预测因子对PAI、PBI和WTI的预测准确率分别比最佳单一预测因子提高9.95%、4.01%和1.96% （AUC值分别提高9.78%、9.45%和2.14%）。这表明结合形态学和生物力学的危险因素可以导致更好的患者筛查策略。(C) 2019 Elsevier B.V.版权所有
Plaque progression prediction is of fundamental significance to cardiovascular research and disease diagnosis, prevention, and treatment. Magnetic resonance image (MRI) data of carotid atherosclerotic plaques were acquired from 20 patients with consent obtained. 3D thin-layer models were constructed to calculate plaque stress and strain. Data for ten morphological and biomechanical risk factors were extracted for analysis. Wall thickness increase (WTI), plaque burden increase (PBI) and plaque area increase (PAI) were chosen as three measures for plaque progression. Generalized linear mixed models (GLMM) with 5-fold cross-validation strategy were used to calculate prediction accuracy and identify optimal predictor. The optimal predictor for PBI was the combination of lumen area (LA), plaque area (PA), lipid percent (LP), wall thickness (WT), maximum plaque wall stress (MPWS) and maximum plaque wall strain (MPWSn) with prediction accuracy = 1.4146 (area under the receiver operating characteristic curve (AUC) value is 0.7158), while PA, plaque burden (PB), WT, LP, minimum cap thickness, MPWS and MPWSn was the best for WTI (accuracy = 1.3140, AUC = 0.6552), and a combination of PA, PB, WT, MPWS, MPWSn and average plaque wall strain (APWSn) was the best for PAI with prediction accuracy = 1.3025 (AUC = 0.6657). The combinational predictors improved prediction accuracy by 9.95%, 4.01% and 1.96% over the best single predictors for PAI, PBI and WTI (AUC values improved by 9.78%, 9.45%, and 2.14%), respectively. This suggests that combining both morphological and biomechanical risk factors could lead to better patient screening strategies. (C) 2019 Elsevier B.V. All rights reserved.