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Abstract
This review examines the potential of chest computed tomography (CT) as a tool for predicting cardiovascular (CV) mortality in patients with lung cancer (LC), a topic of high relevance given the elevated rate of CV death in this patient population. While cancer-related mortality has declined in recent decades, CV mortality among LC patients remains significantly higher than in the general population. Standard CV risk assessment tools do not account for the specific factors associated with cancer and its treatment. This scoping review, conducted in accordance with PRISMA-ScR guidelines, analyzed 68 publications from an initial pool of 869 sources, including original research, systematic reviews, and meta-analyses published up to April 2025. The literature was categorized into three themes: low-dose CT screening and CV risk, perioperative risk assessment, and the use of machine learning for automated risk stratification.
Key prognostic CT markers identified include coronary artery calcification, valvular and aortic calcification, epicardial fat volume and density, and signs of chronic obstructive pulmonary disease (COPD). The integration of chest CT data into clinical practice holds significant potential for improving preventive strategies and reducing CV mortality in cancer patients. Furthermore, the application of machine learning algorithms to CT image analysis offers promising new opportunities for the personalized diagnostics and treatment of LC patients with high CV risk.
References
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