Taohu Zhou,1,2,* Xiuxiu Zhou,1,* Jiong Ni,3 Yu Guan,1 Xin’ang Jiang,1 Xiaoqing Lin,1,4 Jie Li,1,4 Yi Xia,1 Xiang Wang,1 Yun Wang,1 Wenjun Huang,5 Wenting Tu,1 Peng Dong,2 Zhaobin Li,6 Shiyuan Liu,1 Li Fan1 

1Department of Radiology, Second Affiliated Hospital of Naval Medical University, Shanghai, People’s Republic of China; 2School of Medical Imaging, Shandong Second Medical University, Weifang, Shandong, People’s Republic of China; 3Department of Radiology, Tongji Hospital, School of Medicine, Tongji University, Shanghai, People’s Republic of China; 4College of Health Sciences and Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, People’s Republic of China; 5Department of Radiology, The Second People’s Hospital of Deyang, Deyang, Sichuan, People’s Republic of China; 6Department of Radiation Oncology, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, Shanghai, 200233, People’s Republic of China

*These authors contributed equally to this work

Correspondence: Li Fan, Department of Radiology, Second Affiliated Hospital of Naval Medical University, No. 415 Fengyang Road, Shanghai, 200003, People’s Republic of China, Tel +86 21 81886011, Fax +86 2163587668, Email fanli0930@163.com

Background: 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.
Purpose: To develop and validate a radiomics nomogram based on clinical and whole-lung computed tomography (CT) radiomics features to stratify COPD severity.
Patients and Methods: 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.
Results: 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.
Conclusion: 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.

Keywords: chronic obstructive pulmonary disease, radiomics, computed tomography