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Updated: Jul 15, 2026
Three-Dimensional Reconstruction for the Whole Lung with Early Multiple Pulmonary Nodules
Published on: October 13, 2023
Predicting the progression risk of chronic obstructive pulmonary disease in high-risk individuals based on whole-lung
Jianfei Lin1, Yan Zhang2, Sheng Lu3
1Department of Radiology, Huzhou Central Hospital, Fifth School of Clinical Medicine of Zhejiang Chinese Medical University, Huzhou, China.
Background:
As a highly prevalent and seriously debilitating lung disorder, chronic obstructive pulmonary disease (COPD) adds a considerable burden to global health services. However, many early-stage COPD patients do not exhibit abnormal results in lung function tests. Therefore, accurately identifying the individuals with high progression risk who are about to develop clinical COPD has become a key challenge for achieving early, precise intervention and reducing the disease burden. This study aims to develop and validate a comprehensive model for predicting COPD risk in high-risk populations.
Methods:
A retrospective analysis was conducted involving 806 patients from two hospitals, with the time span ranging from January 1, 2021, to May 30, 2025. After the entire lung parenchyma region is automatically segmented from the lung computed tomography (CT) images, imaging biomarker features and deep learning features were extracted. An integrated nomogram was constructed and verified, which combines imaging biomarker characteristics, deep learning attributes, and independent clinical predictors. The performance of the model was evaluated and compared using receiver operating characteristic (ROC) curves, decision curve analysis (DCA), and DeLong tests.
Results:
In the training set and test set, the area under the ROC curve (AUC) values of the clinical model were 0.615 and 0.572, respectively; for the radiomics (Rad) model, they were 0.834 and 0.824, respectively; for the deep learning radiomics (DLR) model, they were 0.873 and 0.825, respectively; those for the combined models were 0.878 and 0.836, respectively. The performance of the combined models was superior to that of the individual clinical model, Rad model, and DLR model.
Conclusions:
This study developed and validated a combined nomogram by integrating the whole-lung Rad features of chest CT with deep learning features, and combining with clinical independent predictors to predict the risk level of high-risk individuals progressing to COPD.
