Single Inspiratory Chest CT-based Generative Deep Learning Models to Evaluate Functional Small Airway Disease.
Authors
Affiliations (6)
Affiliations (6)
- Department of Radiology, Changzheng Hospital, Naval Medical University, 415 Fengyang Rd, Shanghai 200003, The People's Republic of China.
- School of Biomedical Engineering, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, The People's Republic of China.
- Center for Medical Imaging, Robotics, Analytic Computing & Learning (MIRACLE), Suzhou Institute for Advance Research, University of Science and Technology of China, Suzhou, The People's Republic of China.
- Department of Radiology, Ningbo No. 2 Hospital, Ningbo, The People's Republic of China.
- Key Laboratory of Precision and Intelligent Chemistry, University of Science and Technology of China, Hefei, The People's Republic of China.
- Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, The People's Republic of China.
Abstract
<i>"Just Accepted" papers have undergone full peer review and have been accepted for publication in <i>Radiology: Artificial Intelligence</i>. This article will undergo copyediting, layout, and proof review before it is published in its final version. Please note that during production of the final copyedited article, errors may be discovered which could affect the content.</i> Purpose To develop a deep learning model that uses a single inspiratory chest CT scan to generate parametric response maps (PRM) and predict functional small airway disease (fSAD). Materials and Methods In this retrospective study, predictive and generative deep learning models for PRM using inspiratory chest CT were developed using a model development dataset with fivefold cross-validation, with PRM derived from paired respiratory CT as the reference standard. Voxel-wise metrics, including sensitivity, area under the receiver operating characteristic curve (AUC), and structural similarity, were used to evaluate model performance in predicting PRM and expiratory CT images. The best performing model was tested on three internal test sets and an external test set. Results The model development dataset of 308 patients (median age, 67 years, [IQR: 62-70 years]; 113 female) was divided into the training set (<i>n</i> = 216), the internal validation set (<i>n</i> = 31), and the first internal test set (<i>n</i> = 61). The generative model outperformed the predictive model in detecting fSAD (sensitivity 86.3% vs 38.9%; AUC 0.86 vs 0.70). The generative model performed well in the second internal (AUCs of 0.64, 0.84, 0.97 for emphysema, fSAD and normal lung tissue), the third internal (AUCs of 0.63, 0.83, 0.97), and the external (AUCs of 0.58, 0.85, 0.94) test sets. Notably, the model exhibited exceptional performance in the PRISm group of the fourth internal test set (AUC = 0.62, 0.88, and 0.96). Conclusion The proposed generative model, using a single inspiratory CT, outperformed existing algorithms in PRM evaluation, achieved comparable results to paired respiratory CT. Published under a CC BY 4.0 license.