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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, George Howard, Bradley A. Maron, Thomas G. Brott, James F. Meschia, Florence X. Doo, Heng Huang

TL;DR

A new kernel-based additive model is proposed, combining coherence loss with group-sparse regularization for nonlinear classification for nonlinear classification of carotid plaques, revealing a strong association between plaque texture and clinical risk.

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.

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

TL;DR

A new kernel-based additive model is proposed, combining coherence loss with group-sparse regularization for nonlinear classification for nonlinear classification of carotid plaques, revealing a strong association between plaque texture and clinical risk.

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.
Paper Structure (8 sections, 9 equations, 3 figures, 1 table)

This paper contains 8 sections, 9 equations, 3 figures, 1 table.

Table of Contents

  1. Introduction
  2. Methods
  3. CREST-2
  4. Additive Classification Model
  5. Experimental Results
  6. Conclusion
  7. Compliance with Ethical Standards
  8. Acknowledgments

Figures (3)

  • 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).
  • 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.