RS25 - Correlation of Imaging Characteristics of Carotid Plaque with Clinical and Histopathological Features & Application of Artificial Intelligence

Objectives: Stroke is the third leading cause of death. Carotid artery stenosis (CAS) accounts for 8% - 37% of strokes. Landmark carotid trials have shown Carotid Endarterectomy (CEA) superior to best medical therapy (BMT) for symptomatic moderate stenosis and asymptomatic high-grade stenosis. This study aims to correlate imaging characteristics of vulnerable plaque (VP) with clinical and histopathological features and to assess the ability of artificial intelligence (AI) techniques to identify VP and disease burden using duplex ultrasound (DU) data.

Methods: This prospective study from August 2022 - April 2024 used DU and Computerized Tomographic Angiography (CTA) or Magnetic Resonance Angiography (MRA) to assess carotid plaques in patients planned for carotid endarterectomy and subjected plaques to histology. We also imaged carotid plaques using DU in asymptomatic patients on Best Medical Therapy (BMT). YOLOv8 deep learning and U-Net AI models were used to analyse US images.

Results: 150 patients were included in the study. 60 patients (40%) were symptomatic. Age range was 33 – 84 years (Mean 58.9). 115 (76.7%) were males. Hypertension, diabetes, dyslipidemia and smoking were seen in 59%, 56%, 49% and 40% of patients respectively, and were more prevalent in patients with VP (90%, 60%, 70% and 50%). Of the 70 patients who underwent CEA, 60 were symptomatic and 10 asymptomatic. 70% of symptomatic patients had modified RANKIN (mRS) score of 2-3.
Regarding imaging, all surgical patients underwent DU, 49 CTA and 21 MRA. While DU showed VP in 53/60 (88%) symptomatic patients, histology identified VP in 56/60 (93%). Based on this, DU had the highest positive predictive value (PPV) at 95%, followed by MRA 89% and CTA 73%. PPV of DU screening in operated asymptomatic patients was 100%.
AI showed 96% PPV for identifying >50% stenosis. Model training in identifying VP accuracy increased from 0.73 to 0.94 metrics.

Conclusions: DU is an excellent modality to identify nature of carotid plaque and can be a good tool to identify VP in asymptomatic CAS. More data will improve AI accuracy.