Automated Quantification of 3-D Carotid Ultrasound Vessel Wall Texture Change Predicts Vascular Events

Objective To reduce reliance on labor-intensive and operator-dependent explicit plaque segmentation, we evaluated whether a texture-change score (ΔTexture) derived from semi-automated 3-D ultrasound (3-DUS) vessel wall segmentation predicts vascular events better than baseline vessel wall texture (BL-Texture), vessel wall volume change (ΔVWV) and Framingham risk score.Methods 3-DUS images were acquired from 272 patients with carotid atherosclerosis at baseline and 1 y later. Vessel walls were delineated using a validated, minimally user-initialized segmentation workflow. Longitudinal texture change was summarized as vessel wall ΔTexture and compared with BL-Texture, ΔVWV and Framingham risk score. Patients were followed for up to 5 y (median, 3.16 y) for myocardial infarction, transient ischemic attack and stroke.Results ΔTexture and BL-Texture predicted vascular events (log-rank, p Conclusion Vessel wall ΔTexture derived from minimally user-initialized 3-DUS vessel wall segmentation predicted vascular events better than ΔVWV and Framingham risk score. This approach provides a scalable imaging biomarker for vascular event risk assessment without requiring explicit plaque segmentation.