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

Published in Journal of imaging informatics in medicine. Abstract: 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 ...