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

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

SUBMITTER: Wang Q 

PROVIDER: S-EPMC6710108 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature