Combining morphological and biomechanical factors for optimal carotid plaque progression prediction: An MRI-based follow-up study using 3D thin-layer models. (15th October 2019)

• Record Type: Journal Article
• Authors: Wang, Qingyu; Tang, Dalin; Wang, Liang; Canton, Gador; Wu, Zheyang; Hatsukami, Thomas S.; Billiar, Kristen L.; Yuan, Chun
• Is Part Of: International journal of cardiology. Volume 293(2019)
• Page Start: 266
• Page End: 271
• Publication Date: 2019-10-15
• Subjects: AUC Area under of the ROC curve -- APWSn Average plaque wall strain -- APWS Average plaque wall stress -- ECA External carotid artery -- GLMM Generalized linear mixed models -- ICA Internal carotid artery -- LP Lipid percent -- LA Lumen area -- MRI Magnetic resonance image -- MPWSn Maximum plaque wall strain -- MPWS Maximum plaque wall stress -- MinCT Minimum cap thickness -- PA Plaque area -- PAI Plaque area increase -- PB Plaque burden -- PBI Plaque burden increase -- PWSn Plaque wall strain -- PWS Plaque wall stress -- ROC curve Receiver operating characteristic curve -- WT Wall thickness -- WTI Wall thickness increase; Atherosclerotic plaque -- Magnetic resonance imaging (MRI) -- Plaque progression -- Follow-up study -- Carotid artery modeling; Cardiology -- Periodicals; Electronic journals; 616.12
• DOI: 10.1016/j.ijcard.2019.07.005
• Languages: English
• ISSNs: 0167-5273

Abstract

Abstract: 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. Highlights: Combining morphological and mechanical factors can improve plaque progression prediction accuracy by 10%. Plaque burden increase is more predictable than wall thickness increase and plaque area increase (progression measures). 3D thin-layer model requires much less labor cost and correlation results were consistent with 3D full vessel models.