Radiomics differences between GOLD I-II and GOLD III-IV in patients with chronic obstructive pulmonary disease

High-resolution computed tomography (HRCT) serves as an effective imaging modality for characterizing and quantifying structural lung changes associated with chronic obstructive pulmonary disease (COPD). The emerging field of radiomics enables the rapid extraction of numerous quantitative features from medical images, offering considerable promise for supporting clinical decisions. Therefore, the aim of this study was to explore the lung function-associated radiomics features in COPD patients.

This cross-sectional study enrolled 223 patients diagnosed with COPD. The final study cohort comprised 94 patients classified as Global Initiative for Chronic Obstructive Lung Disease (GOLD) stage I-II and 56 as GOLD stage III-IV. For all participants, baseline demographic and clinical characteristics, spirometry data, and chest HRCT images were collected. Subsequently, 944 quantitative radiomics features were extracted from each HRCT scan. To identify features associated with GOLD stages, we employed least absolute shrinkage and selection operator (LASSO) regression for feature selection, followed by logistic regression for modeling. Finally, a nomogram along with its validation curves was constructed to visualize and assess the performance of the predictive model.

Following feature selection via LASSO regression, eight potential predictors were retained. Subsequent binary logistic regression refined this set, revealing two radiomics features (original_firstorder_10Percentile and wavelet.LHL_glszm_GrayLevelVariance) that were independently associated with GOLD stages. The model's discrimination was validated by a C-index of 0.838 and an area under the receiver operating characteristic curve (AUC) of 0.820. Furthermore, decision curve analysis (DCA) confirmed the clinical utility of the nomogram, showing a positive net benefit for decision thresholds from 0.13 to 0.84.

Collectively, a noticeable difference in radiomics features was observed between GOLD I-II and GOLD III-IV patients with COPD. The computed tomography (CT)-based radiomics features, original_firstorder_10Percentile and wavelet.LHL_glszm_GrayLevelVariance, can be potentially used to evaluate the severity of COPD patients. However, our results require further validation through multicenter and large-scale clinical studies.