The potential value of CT-based whole lung radiomics nomogram for predicting osteoporosis risk in COPD patients: a two-center study

To evaluate the value of CT whole-lung imaging histograms in diagnosing osteoporosis (OP) risk in patients with chronic obstructive pulmonary disease (COPD).258 COPD patients were divided into a training cohort (n = 149), an internal validation cohort (n = 64), and an external validation cohort (n = 45). Clinical data and CT results were analyzed. Imaging histologic features of the whole lung were extracted from chest CT images. Machine learning algorithms were utilized to construct the radiomics model. Multifactor logistic regression analysis was used to build the radiomics nomogram by combining independent clinical factors. ROC curves were used to analyze the predictive performance of the models.We developed a model to predict osteoporosis risk in patients with COPD by integrating imaging histologic features, as well as independent clinical risk factors. On the training set, the joint model (area under the curve [AUC], 0.811), the clinical model (AUC, 0.691), and the imaging model (AUC, 0.762). On the internal validation set, the joint model (AUC, 0.806), the clinical model (AUC, 0.724), and the imaging model (AUC, 0.765). On the external validation set, the joint model (AUC, 0.728), the clinical model (AUC, 0.656), and the imaging model (AUC, 0.718). Decision curve analysis showed that the joint model was superior to the single radiomics model with clinical factors. CT-based whole-lung radiomics nomograms are valuable in diagnosing the risk of osteoporosis in patients with COPD.