Long-term carotid plaque progression and the role of intraplaque hemorrhage: A deep learning-based analysis of longitudinal vessel wall imaging

Yin Guo; Ebru Yaman Akcicek; Daniel S Hippe; SeyyedKazem HashemizadehKolowri; Xin Wang; Halit Akcicek; Gador Canton; Niranjan Balu; Duygu Baylam Geleri; Taewon Kim; Dean Shibata; Kaiyu Zhang; Beibei Sun; Xiaodong Ma; Marina S Ferguson; Mahmud Mossa-Basha; Thomas S Hatsukami; Chun Yuan

doi:10.1016/j.jocmr.2025.102670

Long-term carotid plaque progression and the role of intraplaque hemorrhage: A deep learning-based analysis of longitudinal vessel wall imaging

J Cardiovasc Magn Reson. 2025 Dec 10;28(1):102670. doi: 10.1016/j.jocmr.2025.102670. Online ahead of print.

Authors

Yin Guo¹, Ebru Yaman Akcicek², Daniel S Hippe³, SeyyedKazem HashemizadehKolowri², Xin Wang⁴, Halit Akcicek², Gador Canton⁵, Niranjan Balu⁵, Duygu Baylam Geleri⁵, Taewon Kim⁶, Dean Shibata⁵, Kaiyu Zhang¹, Beibei Sun⁷, Xiaodong Ma², Marina S Ferguson⁵, Mahmud Mossa-Basha⁵, Thomas S Hatsukami⁸, Chun Yuan⁹

Affiliations

¹ Department of Bioengineering, University of Washington, Seattle, Washington State, USA.
² Department of Radiology and Imaging Science, University of Utah School of Medicine, Salt Lake City, Utah, USA.
³ Clinical Biostatistics, Clinical Research Division, Fred Hutchison Cancer Center, Seattle, Washington State, USA.
⁴ Department of Electrical and Computer Engineering, University of Washington, Seattle, Washington State, USA.
⁵ Department of Radiology, University of Washington School of Medicine, Seattle, Washington State, USA.
⁶ Department of Radiology, University of Washington School of Medicine, Seattle, Washington State, USA; Department of Neurology, Incheon St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea.
⁷ Department of Radiology, University of Washington School of Medicine, Seattle, Washington State, USA; Department of Radiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
⁸ Department of Surgery, University of Washington School of Medicine, Seattle, Washington State, USA.
⁹ Department of Bioengineering, University of Washington, Seattle, Washington State, USA; Department of Radiology and Imaging Science, University of Utah School of Medicine, Salt Lake City, Utah, USA; Department of Radiology, University of Washington School of Medicine, Seattle, Washington State, USA. Electronic address: cyuan@uw.edu.

Abstract

Background: Carotid atherosclerosis is a major contributor to the etiology of ischemic stroke. Although intraplaque hemorrhage (IPH) is known to increase stroke risk and plaque burden, its long-term effects on plaque dynamics remain unclear. This study aimed to evaluate the long-term impact of IPH on carotid plaque burden progression using deep learning-based segmentation on multi-contrast magnetic resonance vessel wall imaging (VWI).

Methods: 28 asymptomatic subjects with carotid atherosclerosis underwent an average of 4.7±0.6 VWI scans over 5.8±1.1 years. Deep learning pipelines were used to segment the carotid vessel walls and IPH. Bilateral plaque progression was analyzed using correlation coefficients and generalized estimating equations. Associations between IPH occurrence, IPH volume, and plaque burden (%WV) progression were evaluated using linear mixed-effect models.

Results: IPH was detected in 23/50 of the arteries at any time point. Of arteries without IPH at baseline, 11/39 developed new IPH that persisted, while 5/11 arteries with baseline IPH exhibited it throughout the study. Bilateral plaque growth was significantly correlated (r = 0.54, p<0.001), but this symmetry was weakened in cases with IPH (r = 0.1, p = 0.62). Moreover, IPH presence or development at any point was associated with a 2.3% absolute increase in %WV on average within the affected artery (p<0.001). The volume of IPH was also positively associated with increased %WV (p = 0.005).

Conclusion: Deep learning-based segmentation pipelines were utilized to identify IPH, quantify IPH volume, and measure their effects on carotid plaque burden during long-term follow-up. Findings demonstrated that IPH may persist for extended periods. While arteries without IPH demonstrated minimal progression under contemporary treatment, the presence of IPH and greater IPH volume significantly accelerated long-term plaque growth.

Keywords: Carotid atherosclerosis; Deep learning; Intraplaque hemorrhage; Stroke; Vessel wall MRI.