用新模型分析颈动脉斑块，提升卒中风险预测准确率
CRESTomics: Analyzing Carotid Plaques in the CREST-2 Trial with a New Additive Classification Model
- 基于核方法与分组稀疏正则的新增强分类模型
- 在500个斑块上实现高精度风险评估，纹理特征关联性强
- 可视化各特征组贡献，适合临床医生与医学影像研究者
准确识别颈动脉斑块对预防颈动脉狭窄患者卒中至关重要。本研究分析了来自多中心临床试验CREST-2的500个斑块，从B-mode超声图像中提取放射组学特征，寻找与高风险相关的标志物。提出一种基于核函数的新增强分类模型，结合相干性损失与分组稀疏正则化，实现非线性分类。通过部分依赖图可视化每组特征的累加效应。结果表明，该方法在评估斑块风险时兼具高准确性与可解释性，揭示了斑块纹理与临床风险之间的强关联。
原文摘要 · Abstract (English)
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.
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