CRESTomics: Analyzing Carotid Plaques in the CREST-2 Trial with a New Additive Classification Model

Pranav Kulkarni; Brajesh K. Lal; Georges Jreij; Sai Vallamchetla; Langford Green; Jenifer Voeks; John Huston; Lloyd Edwards

cs.LGMar 4, 2026

CRESTomics: Analyzing Carotid Plaques in the CREST-2 Trial with a New Additive Classification Model

Authors: Pranav Kulkarni, Brajesh K. Lal, Georges Jreij, Sai Vallamchetla, Langford Green, Jenifer Voeks, John Huston, Lloyd Edwards, +6 more

Organizations: University of Maryland Institute for Health Computing · University of Maryland, College Park · University of Maryland School of Medicine · Mayo Clinic · Medical University of South Carolina · University of Alabama at Birmingham

Abstract

Accurate characterization of carotid plaques is critical for stroke prevention in patients with carotid stenosis. We analyze 500 plaques from CREST-2, a multi-center clinical trial, to identify radiomics-based markers from B-mode ultrasound images linked with high-risk. We propose a new kernel-based additive model, combining coherence loss with group-sparse regularization for nonlinear classification. Group-wise additive effects of each feature group are visualized using partial dependence plots. Results indicate our method accurately and interpretably assesses plaques, revealing a strong association between plaque texture and clinical risk.

Figures & tables

Figure 1: High- and low-risk carotid plaque on B-mode image.

Figure 2: Pearson’s $r$ between radiomics (x-axis) and plaque characteristics (y-axis).

Method	AUROC $\uparrow$	ACC $\uparrow$	F1 $\uparrow$
Logistic	0.93 (0.01)	96.00 (1.26)	79.52 (7.69)
L1SVM	0.90 (0.02)	96.00 (1.26)	79.52 (7.69)
L2SVM	0.90 (0.02)	96.00 (1.26)	79.52 (7.69)
GaussianSVM	0.94 (0.02)	97.20 (0.98)	86.55 (5.34)
XGBoost	0.93 (0.03)	96.40 (0.80)	82.18 (4.36)
GAM	0.92 (0.01)	96.80 (0.98)	84.36 (5.34)

Table 1: Performance of classification models on CREST-2. Statistically significant differences are underlined.

Figure 3: (A) Partial dependence plots for radiomic feature groups. Positive dependence indicates higher risk contributions, while negative dependence indicates lower risk contributions. (B) Group importance across 5-fold cross-validation.

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A CUBS-Compatible Ultrasound Morphology and Uncertainty-Aware Baseline for Carotid Intima-Media Segmentation and Preliminary Risk Prediction

Aueaphum Aueawatthanaphisut

School of Information, Computer, and Communication Technology Sirindhorn International Institute of Technology, Thammasat University Pathum Thani, Thailand

Ultrasound Image Segmentation Clinical Risk Prediction

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Carotid atherosclerosis is a major contributor to ischemic stroke and transient ischemic attack. Conventional ultrasound assessment is commonly based on intima-media thickness, plaque appearance, stenosis degree, and peak systolic velocity, but these morphology- and velocity-based indicators may not fully capture patient-specific vascular risk. This study presents AtheroFlow-XNet, a CUBS-compatible ultrasound morphology and uncertainty-aware learning baseline for carotid intima-media segmentation and preliminary risk prediction. Using the Carotid Ultrasound Boundary Study dataset, manual lumen-intima and media-adventitia boundary annotations were converted into dense intima-media masks for supervised segmentation. Clinical variables were incorporated into an auxiliary risk-prediction branch, and Monte Carlo dropout was used for uncertainty-aware inference. The model was evaluated using a patient-level train-validation-test split with 1,522 training images, 326 validation images, and 328 testing images. The proposed model achieved a Dice coefficient of 0.7930 for LI-MA mask segmentation, a segmentation loss of 0.2359, and an area under the receiver operating characteristic curve of 0.6910 for preliminary risk prediction. Qualitative results showed that predicted masks were generally aligned with manual annotations, while uncertainty maps highlighted ambiguous wall-boundary regions. These results suggest that ultrasound-derived carotid morphology can support automated wall analysis and uncertainty-aware interpretation. Since CUBS does not provide Doppler waveforms or CFD-derived hemodynamic biomarkers, this work should be interpreted as a reproducible morphology-driven baseline. Future work will incorporate Doppler-derived flow profiles, patient-specific vascular reconstruction, and CFD-based wall shear biomarkers.

Abstract

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