Abstract
BACKGROUND: The aim of this study was to evaluate an integrated fluorine-18 fluorodeoxyglucose positron emission tomography/magnetic resonance imaging (PET/MRI) model combining conventional and radiomic features for the noninvasive identification of symptomatic extracranial carotid atherosclerotic plaques. METHODS: A total of 200 patients with advanced carotid plaques (78 symptomatic, 122 asymptomatic) underwent hybrid fluorine-18 fluorodeoxyglucose PET/MRI. Six MRI morphological features (intraplaque hemorrhage, lipid-rich necrotic core, ruptured fibrous cap, calcification, surface ulceration, and plaque enhancement) and 3 PET metabolic parameters-metabolic uptake, maximum standardized uptake value, and target-to-background ratio-were evaluated. A total of 1991 radiomics were extracted from each of the MRI and PET images, respectively. For each modality, 3 types of models were developed: conventional, radiomics, and combined (incorporating conventional and radiomic features). Diagnostic performance was evaluated using multivariate logistic regression and a linear support vector classifier. RESULTS: In MRI-based analysis, surface ulceration and plaque enhancement emerged as independent predictors of symptomatic plaques. In PET-based analysis, standardized uptake value and metabolic uptake were identified as significant independent predictors. The PET conventional model outperformed the MRI conventional model in discriminative ability (areas under the curve: training, 0.899 versus 0.755; internal test, 0.926 versus 0.822; temporal validation, 0.878 versus 0.702). The integrated PET/MRI combined model achieved the best performance, with areas under the curve of 0.962 (training), 0.967 (internal test), and 0.926 (temporal validation), significantly outperforming PET- and MRI-based combined models (all P<0.05). CONCLUSIONS: The fluorine-18 fluorodeoxyglucose PET/MRI combined model integrating morphological, metabolic, and radiomic features outperformed other models, supporting its potential as a noninvasive, high-precision tool for cerebrovascular risk stratification.