Li Yuwei, Lin Chengting, Cui Lei, Huang Chao, Shi Liting, Huang Shiyang, Yu Yue, Zhou Xianglan, Zhou Qian, Chen Kun, Shi Lei
Department of Epidemiology, School of Public Health, Nanjing Medical University, Nanjing, 211166, China.
Department of Radiology, Zhejiang Cancer Hospital, Hangzhou, 310022, China.
BMC Med Imaging. 2025 Jun 5;25(1):204. doi: 10.1186/s12880-025-01747-5.
Previous studies have highlighted the prominent role of age in lung cancer risk, with signs of lung aging visible in computed tomography (CT) imaging. This study aims to characterize lung aging using quantitative radiomic features extracted from five delineated lung lobes and explore how age contributes to lung cancer development through these features.
We analyzed baseline CT scans from the Wenling lung cancer screening cohort, consisting of 29,810 participants. Deep learning-based segmentation method was used to delineate lung lobes. A total of 1,470 features were extracted from each lobe. The minimum redundancy maximum relevance algorithm was applied to identify the top 10 age-related radiomic features among 13,137 never smokers. Multiple regression analyses were used to adjust for confounders in the association of age, lung lobar radiomic features, and lung cancer. Linear, Cox proportional hazards, and parametric accelerated failure time models were applied as appropriate. Mediation analyses were conducted to evaluate whether lobar radiomic features mediate the relationship between age and lung cancer risk.
Age was significantly associated with an increased lung cancer risk, particularly among current smokers (hazard ratio = 1.07, P = 2.81 × 10). Age-related radiomic features exhibited distinct effects across lung lobes. Specifically, the first order mean (mean attenuation value) filtered by wavelet in the right upper lobe increased with age (β = 0.019, P = 2.41 × 10), whereas it decreased in the right lower lobe (β = -0.028, P = 7.83 × 10). Three features, namely wavelet_HL_firstorder_Mean of the right upper lobe, wavelet_LH_firstorder_Mean of the right lower lobe, and original_shape_MinorAxisLength of the left upper lobe, were independently associated with lung cancer risk at Bonferroni-adjusted P value. Mediation analyses revealed that density and shape features partially mediated the relationship between age and lung cancer risk while a suppression effect was observed in the wavelet first order mean of right upper lobe.
The study reveals lobe-specific heterogeneity in lung aging patterns through radiomics and their associations with lung cancer risk. These findings may contribute to identify new approaches for early intervention in lung cancer related to aging.
Not applicable.
先前的研究强调了年龄在肺癌风险中的突出作用,在计算机断层扫描(CT)成像中可见肺老化的迹象。本研究旨在利用从五个划定的肺叶中提取的定量放射组学特征来表征肺老化,并探讨年龄如何通过这些特征促进肺癌的发生发展。
我们分析了来自温岭肺癌筛查队列的29810名参与者的基线CT扫描。基于深度学习的分割方法用于划定肺叶。每个肺叶共提取1470个特征。应用最小冗余最大相关算法在13137名从不吸烟者中识别出前10个与年龄相关的放射组学特征。多元回归分析用于调整年龄、肺叶放射组学特征和肺癌之间关联中的混杂因素。根据情况应用线性、Cox比例风险和参数加速失效时间模型。进行中介分析以评估肺叶放射组学特征是否介导年龄与肺癌风险之间的关系。
年龄与肺癌风险增加显著相关,尤其是在当前吸烟者中(风险比 = 1.07,P = 2.81×10)。与年龄相关的放射组学特征在不同肺叶表现出不同的影响。具体而言,右上叶经小波滤波后的一阶均值(平均衰减值)随年龄增加(β = 0.019,P = 2.41×10),而右下叶则下降(β = -0.028,P = 7.83×10)。在Bonferroni校正的P值下,三个特征,即右上叶的小波_HL_一阶均值、右下叶的小波_LH_一阶均值和左上叶的原始形状_短轴长度,与肺癌风险独立相关。中介分析显示,密度和形状特征部分介导了年龄与肺癌风险之间的关系,而右上叶的小波一阶均值存在抑制作用。
该研究通过放射组学揭示了肺老化模式中肺叶特异性的异质性及其与肺癌风险的关联。这些发现可能有助于确定与衰老相关的肺癌早期干预的新方法。
不适用。