Kim Eun-Ju, Cho Seong Woo, Yang Jung-Ho, Jeong Won Gi
Department of Radiology, Chonnam National University Medical School and Chonnam National University Hwasun Hospital, Hwasun, Republic of Korea.
Department of Radiology, Armed Forces Daejeon Hospital, Daejeon, Republic of Korea.
Clin Imaging. 2025 Sep;125:110558. doi: 10.1016/j.clinimag.2025.110558. Epub 2025 Jul 6.
The clinical implications of coronary artery calcification (CAC) growth remain underexplored. This study aims to assess CAC growth and its association with adverse cardiovascular events (ACEs) in individuals undergoing lung cancer screening (LCS) using artificial intelligence (AI)-assisted evaluation.
We included patients who underwent LCS low-dose chest CT (LDCT) between April 2017 and December 2023 with available follow-up LDCT scans. CAC severity was quantified using AI-based software. CAC growth was defined as incident CAC in those with baseline CAC = 0 or annual progression > 15 % in those with baseline CAC > 0. ACEs were categorized as major or minor events. Associations between CAC growth and ACEs were evaluated using Cox regression models, adjusting for baseline age and CAC status.
Male patients (n = 193; mean age, 61.6 ± 5.2 years) were analyzed. Over a 4-year mean follow-up, 15.5 % experienced ACEs (major event: 4.1 %, minor event: 11.4 %). Greater baseline CAC severity correlated with a higher annual CAC increase (p < 0.001). Age (adjusted hazard ratio (HR) (95 % confidence interval (CI)) = 1.08 (1.00, 1.17); p = 0.041), CAC growth (adjusted HR (95 % CI) = 2.40 (1.13, 5.09); p = 0.023), and moderate to severe baseline CAC (adjusted HR (95 % CI) = 2.86 (1.11, 7.38); p = 0.030) in the three-tiered classification were significantly associated with a higher occurrence of total ACEs.
AI-assisted CAC growth tracking using serial LDCT scans provides prognostic value in LCS populations and may guide risk-based cardiovascular follow-up and prevention strategies in clinical practice.
冠状动脉钙化（CAC）进展的临床意义仍未得到充分探索。本研究旨在使用人工智能（AI）辅助评估，评估肺癌筛查（LCS）个体的CAC进展及其与不良心血管事件（ACEs）的关联。
我们纳入了2017年4月至2023年12月期间接受LCS低剂量胸部CT（LDCT）检查且有可用随访LDCT扫描的患者。使用基于AI的软件对CAC严重程度进行量化。CAC进展定义为基线CAC = 0者出现新发CAC，或基线CAC>0者年度进展>15%。ACEs分为主要事件或次要事件。使用Cox回归模型评估CAC进展与ACEs之间的关联，并对基线年龄和CAC状态进行调整。
分析了男性患者（n = 193；平均年龄，61.6±5.2岁）。在平均4年的随访中，15.5%的患者发生了ACEs（主要事件：4.1%，次要事件：11.4%）。更高的基线CAC严重程度与更高的年度CAC增加相关（p < 0.001）。在三级分类中，年龄（调整后风险比（HR）（95%置信区间（CI））= 1.08（1.00，1.17）；p = 0.041）、CAC进展（调整后HR（95%CI）= 2.40（1.13，5.09）；p = 0.023）和中度至重度基线CAC（调整后HR（95%CI）= 2.86（1.11，7.38）；p = 0.030）与总ACEs的更高发生率显著相关。
使用系列LDCT扫描进行AI辅助的CAC进展追踪在LCS人群中具有预后价值，并可能在临床实践中指导基于风险的心血管随访和预防策略。
