WCLC 2025 - Posters & ePosters-P3.01.02 Association Between Age and Lung Cancer Risk: Evidence From Lung Lobar Radiomics

Pdf Summary

This study investigates the association between age-related changes in lung tissue, quantified using CT-based radiomic features from specific lung lobes, and lung cancer risk. Utilizing baseline CT scans from 29,810 participants, the researchers applied a deep learning segmentation to delineate lung lobes and extracted 1,470 radiomic features per lobe. Focusing on 13,137 non-smokers, they identified the top 10 age-related radiomic features using a minimum redundancy maximum relevance algorithm.<br /><br />After adjusting for confounders including sex, BMI, education, smoking history, occupational exposures, chronic respiratory diseases, and family lung cancer history, multiple regression models confirmed significant associations between increasing age and specific lobar radiomic features reflecting lung aging. Notably, these features represent changes in density and texture patterns across lobes: for instance, increased density in the right upper lobe and decreased density in the right lower lobe, and larger minor axis length in the left upper lobe.<br /><br />Mediation analyses showed that certain density and shape-related radiomic features partially mediate the relationship between age and lung cancer risk, suggesting that age-related lung tissue alterations detected radiologically contribute to carcinogenesis risk. However, a suppression effect was observed in a wavelet first-order feature of the right upper lobe, indicating complex lobe-specific patterns.<br /><br />The study underscores that radiomic quantification of lobar lung aging serves as an objective marker linking aging to lung cancer development. Future research directions include exploring the underlying biological mechanisms that connect radiomic aging features, such as density and texture changes, to carcinogenesis pathways. Additionally, integrating these radiomic aging markers into lung cancer risk prediction models and screening protocols could enhance early detection.<br /><br />In summary, this large-scale radiomics analysis provides novel evidence that lobar-specific quantitative imaging features reflecting lung aging partly explain how advancing age elevates lung cancer risk, offering potential biomarkers for improved screening strategies.

Asset Subtitle

Yuwei Li

Meta Tag

Speaker Yuwei Li

Topic Risk Factors, Risk Reduction & Tobacco Control

Keywords

lung cancer risk

age-related lung changes

CT radiomics

lung lobe segmentation

non-smokers

radiomic features

lung tissue density

texture analysis

mediation analysis

lung cancer screening