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 a natural-language rationale. It integrates three components: a Pulmonary Perception Module that summarizes lung abnormalities, an Agentic Pulmonary-to-Cardiac Reasoning Module that infers their cardiovascular implications, and a Cardiac Feature Extractor 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 (AUC=0.919) and mortality prediction (AUC=0.838), 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）本质上同时捕获肺部与心脏结构，为肺部和心血管健康的联合评估提供了独特机遇。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学生物标志物。我们提出一种可解释性跨疾病推理框架，能够从单次LDCT扫描中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体推理过程：首先感知肺部征象，继而通过既定医学知识进行推理，最终得出具有自然语言依据的心血管判断。它整合了三个组件：总结肺部异常的肺部感知模块、推断其心血管影响的智能体肺-心推理模块，以及编码结构生物标志物的心脏特征提取器。这些组件的输出被融合以产生兼具准确性与生理学依据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（AUC=0.919）和死亡率预测（AUC=0.838）方面达到最先进性能，优于单疾病及纯影像学基线方法。除量化提升外，该框架提供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间的内在关联。总体而言，本研究为LDCT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。