Single Inspiratory Chest CT-based Generative Deep Learning Models to Evaluate Functional Small Airways Disease

doi:10.1148/ryai.240680

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	Single Inspiratory Chest CT-based Generative Deep Learning Models to Evaluate Functional Small Airways Disease
	Zhang, Di 1; Zhao, Mingyue 2,3; Zhou, Xiuxiu 1; Li, Yiwei 2,3; Guan, Yu 1; Xia, Yi 1; Zhang, Jin 1; Dai, Qi 6; Zhang, Jingfeng 6; Fan, Li 1; Zhou, S. Kevin 2,3,4,5; Liu, Shiyuan 1
	2025-09-01
发表期刊	RADIOLOGY-ARTIFICIAL INTELLIGENCE
ISSN	2638-6100
卷号	7 期号:5 页码:11
摘要	Purpose To develop a deep learning model that uses a single inspiratory chest CT scan to perform parametric response mapping (PRM) and predict functional small airways 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. Voxelwise metrics, including sensitivity, area under the receiver operating characteristic curve (AUC), and structural similarity index measure, were used to evaluate model performance in predicting PRM and generating 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 individuals (median age, 67 years [IQR: 62-70 years]; 113 female) was divided into the training set (n = 216), the internal validation set (n = 31), and the first internal test set (n = 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, and 0.97 for emphysema, fSAD, and normal lung tissue, respectively), the third internal (AUCs of 0.63, 0.83, and 0.97), and the external (AUCs of 0.58, 0.85, and 0.94) test sets. Notably, the model exhibited exceptional performance in the preserved ratio impaired spirometry group of the fourth internal test set (AUCs of 0.62, 0.88, and 0.96). Conclusion The proposed generative model, using a single inspiratory CT scan, outperformed existing algorithms in PRM evaluation and achieved comparable results to paired respiratory CT.
DOI	10.1148/ryai.240680
收录类别	SCI
语种	英语
WOS研究方向	Computer Science ; Radiology, Nuclear Medicine & Medical Imaging
WOS类目	Computer Science, Artificial Intelligence ; Radiology, Nuclear Medicine & Medical Imaging
WOS记录号	WOS:001621426600002
出版者	RADIOLOGICAL SOC NORTH AMERICA (RSNA)
引用统计
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/42934
专题	中国科学院计算技术研究所
通讯作者	Fan, Li
作者单位	1.Naval Med Univ, Changzheng Hosp, Dept Radiol, 415 Fengyang Rd, Shanghai 200003, Peoples R China 2.Univ Sci & Technol China, Sch Biomed Engn, Div Life Sci & Med, Hefei, Peoples R China 3.Univ Sci & Technol China, Suzhou Inst Adv Res, Ctr Med Imaging Robot Analyt Comp & Learning MIRAC, Suzhou, Peoples R China 4.Univ Sci & Technol China, Key Lab Precis & Intelligent Chem, Hefei, Peoples R China 5.Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc Chinese Acad Sci, Beijing, Peoples R China 6.Ningbo 2 Hosp, Dept Radiol, Ningbo, Peoples R China
推荐引用方式 GB/T 7714	Zhang, Di,Zhao, Mingyue,Zhou, Xiuxiu,et al. Single Inspiratory Chest CT-based Generative Deep Learning Models to Evaluate Functional Small Airways Disease[J]. RADIOLOGY-ARTIFICIAL INTELLIGENCE,2025,7(5):11.
APA	Zhang, Di.,Zhao, Mingyue.,Zhou, Xiuxiu.,Li, Yiwei.,Guan, Yu.,...&Liu, Shiyuan.(2025).Single Inspiratory Chest CT-based Generative Deep Learning Models to Evaluate Functional Small Airways Disease.RADIOLOGY-ARTIFICIAL INTELLIGENCE,7(5),11.
MLA	Zhang, Di,et al."Single Inspiratory Chest CT-based Generative Deep Learning Models to Evaluate Functional Small Airways Disease".RADIOLOGY-ARTIFICIAL INTELLIGENCE 7.5(2025):11.