Associations between carotid atherosclerotic plaque characteristics determined by magnetic resonance imaging and improvement of cognition in patients undergoing carotid endarterectomy

Abstract

Background: To determine the predictive value of carotid plaque characteristics for the improvement of cognition in patients with moderate-to-severe carotid stenosis after carotid endarterectomy (CEA), using vessel wall magnetic resonance imaging (MRI).Methods: This was a prospective cohort study. Patients with unilateral, moderate-to-severe carotid stenosis referred to the Peking University Third Hospital for CEA were prospectively recruited and underwent carotid vessel wall MRI within 1 week before CEA. We performed Montreal Cognitive Assessment (MoCA) within 1 week before and 3-4 days after CEA. The morphological and compositional characteristics of carotid plaques on MRI were evaluated. Improvement of cognition was defined as >10% increase of the total MoCA score after CEA compared with baseline. Carotid plaque characteristics were compared between patients with and without cognitive improvement. Results: In total, 105 patients (91 males; mean age, 65.5±8.4 years) were included. The volume {48.0 [interquartile range (IQR), 21.0 to 91.6] vs. 16.3 (IQR, 8.1 to 53.1) mm 3 ; P=0.005} and cumulative slice [4.0 (IQR, 3.0 to 7.0) vs. 3.0 (IQR, 2.0 to 5.0); P=0.019] of carotid calcification, and maximum percentage of calcification area [13.1% (IQR, 6.0% to 19.8%) vs. 6.2% (IQR, 3.7% to 10.8%); P=0.004] were significantly smaller in participants with cognitive improvement compared to those without. Univariate logistic regression analysis showed that volume [odds ratio (OR) =0.

Integrating fuzzy metrics and negation operator in FCM algorithm via genetic algorithm for MRI image segmentation

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“…Moreover, research exploring the associations between carotid atherosclerotic plaque characteristics and cognitive improvement postsurgery sheds light on the intricate interplay between vascular health and neurological function. [15] To further establish the soundness of our study, we will also integrate findings from studies such as the investigation into anatomical characteristics affecting surgical approaches in lumbar fusion procedures and the image-based visualization of stents in mechanical thrombectomy for acute ischemic stroke cases. [16,17] Overall, compared with artificial neural networks with a single hidden layer, deep learning has stronger expressive and feature-learning abilities.…”

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