Radiomic Signatures of Smoking in Visually Normal Lung Parenchyma: Bilateral CT Analysis Using Machine Learning

Smoking is associated with structural and functional alterations in the lungs, some of which may be reflected in quantitative imaging features. Radiomics enables the extraction of high-dimensional image-derived descriptors and may provide information beyond routinely available clinical variables. We investigated whether bilateral lung radiomics, both independently and integrated with clinical variables, could classify patient-level smoking status. An exploratory secondary analysis was also performed to assess whether radiomic and clinical feature representations could discriminate smoking exposure intensity among smokers. A total of 506 patients were included, comprising 253 smokers and 253 nonsmokers. Right and left lung radiomic features were extracted separately from visually normal-appearing lung parenchyma on thoracic CT images. Multiple feature representations were evaluated, including right lung radiomics, left lung radiomics, concatenated right-left radiomics, summary/asymmetry radiomics, clinical variables alone, and radiomics combined with clinical variables. Clinical variables included age, sex, height, weight, and body mass index. Pack-years were excluded from the binary smoking status classification models to avoid target-related information leakage. For each binary classification task, a balanced internal hold-out set containing 25 smokers and 25 nonsmokers was reserved, while the remaining patients were evaluated using stratified tenfold cross-validation. Four feature selection or dimensionality reduction strategies and five classifier families were assessed, resulting in 435 model configurations across the main cohort and sex-specific subgroup analyses. Threshold selection was performed using the Youden index derived from cross-validated out-of-fold predictions. For the exploratory smoking exposure analysis, 253 smokers with available pack-year information were categorized into < 10, 10-20, and > 20 pack-year groups and evaluated using pooled stratified tenfold cross-validation. The best-performing radiomics-based configurations showed higher discrimination than the clinical-only configuration. The clinical-only random forest model achieved a cross-validation ROC-AUC of 0.673 and a hold-out ROC-AUC of 0.610. Among radiomics-only configurations, concatenated bilateral radiomics achieved a cross-validation ROC-AUC of 0.734 and a hold-out ROC-AUC of 0.712. The strongest hold-out discrimination was obtained using concatenated bilateral radiomics combined with clinical variables, ReliefF feature selection, and stacking classification, achieving a hold-out ROC-AUC of 0.803, balanced accuracy of 0.700, sensitivity of 0.720, and specificity of 0.680. Feature selection stability analysis showed that the final concatenated bilateral radiomics + clinical configuration retained five clinical variables and 15 radiomic features, several of which were repeatedly selected across cross-validation folds. Sex-specific subgroup analyses showed moderate but variable performance. In the exploratory three-class smoking exposure analysis, integrating left lung radiomics with clinical variables yielded the most balanced class-wise performance, achieving the highest balanced accuracy (0.658) and the highest sensitivity for the intermediate 10-20 pack-year group (0.417), although the clinical-only model achieved the highest macro-AUC. Bilateral lung radiomics showed potential for patient-level smoking status classification, particularly when combined with clinical variables. These findings suggest that quantitative imaging features could provide complementary information beyond routinely available patient-level variables. The exploratory smoking exposure analysis further suggested that radiomics + clinical configurations may contain limited information related to smoking exposure intensity, although intermediate exposure remained challenging to discriminate. However, the moderate performance, subgroup variability, and lack of external validation indicate that this approach should be considered investigational until confirmed in larger independent cohorts.