Low-dose chest computed tomography (LDCT) inherently captures both pulmonary and cardiac structures, offering a unique opportunity for joint assessment of lung and cardiovascular health. However, most existing approaches treat these domains as independent tasks, overlooking their physiological interplay and shared imaging biomarkers. We propose an Explainable Cross-Disease Reasoning Framework that enables interpretable cardiopulmonary risk assessment from a single LDCT scan. The framework introduces an agentic reasoning process that emulates clinical diagnostic thinking-first perceiving pulmonary findings, then reasoning through established medical knowledge, and finally deriving a cardiovascular judgment with explanatory rationale. It integrates three synergistic components: a pulmonary perception module that summarizes lung abnormalities, a knowledge-guided reasoning module that infers their cardiovascular implications, and a cardiac representation module that encodes structural biomarkers. Their outputs are fused to produce a holistic cardiovascular risk prediction that is both accurate and physiologically grounded. Experiments on the NLST cohort demonstrate that the proposed framework achieves state-of-the-art performance for CVD screening and mortality prediction, outperforming single-disease and purely image-based baselines. Beyond quantitative gains, the framework provides human-verifiable reasoning that aligns with cardiological understanding, revealing coherent links between pulmonary abnormalities and cardiac stress mechanisms. Overall, this work establishes a unified and explainable paradigm for cardiovascular analysis from LDCT, bridging the gap between image-based prediction and mechanism-based medical interpretation.

翻译：低剂量胸部计算机断层扫描（LDCT）本质上同时捕捉了肺部与心脏结构，为联合评估肺部和心血管健康提供了独特机会。然而，现有方法大多将这两个领域视为独立任务，忽视了其生理相互作用及共享的影像生物标志物。我们提出一种可解释的跨疾病推理框架，能够通过单次LDCT扫描实现可解释的心肺风险评估。该框架引入了一种模拟临床诊断思维的智能推理过程：先感知肺部征象，再通过既定医学知识进行推理，最终得出附带解释性依据的心血管判断。它整合了三个协同组件：总结肺部异常的肺部分析模块、推断其心血管影响的知识引导推理模块，以及编码结构生物标志物的心脏表征模块。这些组件的输出被融合以生成既准确又基于生理学的整体心血管风险预测。在NLST队列上的实验表明，所提框架在心血管疾病筛查和死亡率预测方面达到了最先进的性能，优于单疾病及纯影像基线方法。除量化提升外，该框架提供了符合心脏病学认知的人类可验证推理，揭示了肺部异常与心脏应激机制之间的内在联系。总体而言，本研究为基于LDCT的心血管分析建立了一个统一且可解释的范式，弥合了基于图像的预测与基于机制的医学解释之间的鸿沟。