A CT-Based Lung Radiomics Nomogram for Classifying the Severity of Chronic Obstructive Pulmonary Disease

Chronic obstructive pulmonary disease (COPD) is a major global health concern, and while traditional pulmonary function tests are effective, recent radiomics advancements offer enhanced evaluation by providing detailed insights into the heterogeneous lung changes.

To develop and validate a radiomics nomogram based on clinical and whole-lung computed tomography (CT) radiomics features to stratify COPD severity.

One thousand ninety-nine patients with COPD (including 308, 132, and 659 in the training, internal and external validation sets, respectively), confirmed by pulmonary function test, were enrolled from two institutions. The whole-lung radiomics features were obtained after a fully automated segmentation. Thereafter, a clinical model, radiomics signature, and radiomics nomogram incorporating radiomics signature as well as independent clinical factors were constructed and validated. Additionally, receiver-operating characteristic (ROC) curve, area under the ROC curve (AUC), decision curve analysis (DCA), and the DeLong test were used for performance assessment and comparison.

In comparison with clinical model, both radiomics signature and radiomics nomogram outperformed better on COPD severity (GOLD I-II and GOLD III-IV) in three sets. The AUC of radiomics nomogram integrating age, height and Radscore, was 0.865 (95% CI, 0.818-0.913), 0.851 (95% CI, 0.778-0.923), and 0.781 (95% CI, 0.740-0.823) in three sets, which was the highest among three models (0.857; 0.850; 0.774, respectively) but not significantly different (P > 0.05). Decision curve analysis demonstrated the superiority of the radiomics nomogram in terms of clinical usefulness.

The present work constructed and verified the novel, diagnostic radiomics nomogram for identifying the severity of COPD, showing the added value of chest CT to evaluate not only the pulmonary structure but also the lung function status.