Cardiovascular diseases (CVD) remain a leading health concern and contribute significantly to global mortality rates. While clinical advancements have led to a decline in CVD mortality, accurately identifying individuals who could benefit from preventive interventions remains an unsolved challenge in preventive cardiology. Current CVD risk prediction models, recommended by guidelines, are based on limited traditional risk factors or use CT imaging to acquire quantitative biomarkers, and still have limitations in predictive accuracy and applicability. On the other hand, end-to-end trained CVD risk prediction methods leveraging deep learning on CT images often fail to provide transparent and explainable decision grounds for assisting physicians. In this work, we proposed a novel joint representation that integrates discrete quantitative biomarkers and continuous deep features extracted from chest CT scans. Our approach initiated with a deep CVD risk classification model by capturing comprehensive continuous deep learning features while jointly obtaining currently clinical-established quantitative biomarkers via segmentation models. In the feature joint representation stage, we use an instance-wise feature-gated mechanism to align the continuous and discrete features, followed by a soft instance-wise feature interaction mechanism fostering independent and effective feature interaction for the final CVD risk prediction. Our method substantially improves CVD risk predictive performance and offers individual contribution analysis of each biomarker, which is important in assisting physicians' decision-making processes. We validated our method on a public chest low-dose CT dataset and a private external chest standard-dose CT patient cohort of 17,207 CT volumes from 6,393 unique subjects, and demonstrated superior predictive performance, achieving AUCs of 0.875 and 0.843, respectively.

翻译：心血管疾病（CVD）仍然是主要的健康问题，对全球死亡率有显著影响。尽管临床进展已使CVD死亡率有所下降，但准确识别可从预防性干预中获益的个体，仍然是预防心脏病学中一个未解决的挑战。当前指南推荐的CVD风险预测模型，基于有限传统风险因素或使用CT成像获取定量生物标志物，在预测准确性和适用性方面仍存在局限。另一方面，利用CT图像进行端到端训练的CVD风险预测方法，通常无法为辅助医生决策提供透明且可解释的判断依据。在本工作中，我们提出了一种新颖的联合表征方法，整合了从胸部CT扫描中提取的离散定量生物标志物与连续深度特征。我们的方法首先通过捕获全面的连续深度学习特征构建深度CVD风险分类模型，同时借助分割模型联合获取当前临床已确立的定量生物标志物。在特征联合表征阶段，我们采用实例级特征门控机制对齐连续与离散特征，随后通过软实例级特征交互机制促进独立且有效的特征交互，以完成最终的CVD风险预测。我们的方法显著提升了CVD风险预测性能，并提供各生物标志物的个体贡献分析，这对于辅助医生决策过程至关重要。我们在一个公共胸部低剂量CT数据集和一个包含6,393名受试者共17,207个CT体积的私有外部胸部标准剂量CT患者队列上验证了该方法，并展示了卓越的预测性能，分别取得了0.875和0.843的AUC值。