中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
首页 机构 成果 学者
  
  1. CAS IR Grid
  2. 计算技术研究所
  3. 中国科学院计算技术研究所
Single Inspiratory Chest CT-based Generative Deep Learning Models to Evaluate Functional Small Airways Disease

文献类型：期刊论文

作者Zhang, Di4; Zhao, Mingyue2,3; Zhou, Xiuxiu4; Li, Yiwei2,3; Guan, Yu4; Xia, Yi4; Zhang, Jin4; Dai, Qi1; Zhang, Jingfeng1; Fan, Li4
刊名RADIOLOGY-ARTIFICIAL INTELLIGENCE
出版日期2025-09-01
卷号7期号:5页码:11
ISSN号2638-6100
DOI10.1148/ryai.240680
英文摘要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.
资助项目National Natural Science Foundation of China[82430065] ; National Natural Science Foundation of China[82171926]
WOS研究方向Computer Science ; Radiology, Nuclear Medicine & Medical Imaging
语种英语
WOS记录号WOS:001621426600002
出版者RADIOLOGICAL SOC NORTH AMERICA (RSNA)
源URL[http://119.78.100.204/handle/2XEOYT63/42933]  
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.Ningbo 2 Hosp, Dept Radiol, Ningbo, 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.Naval Med Univ, Changzheng Hosp, Dept Radiol, 415 Fengyang Rd, Shanghai 200003, Peoples R China
5.Univ Sci & Technol China, Key Lab Precis & Intelligent Chem, Hefei, Peoples R China
6.Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc Chinese Acad Sci, Beijing, 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.

入库方式： OAI收割

来源：计算技术研究所

浏览0
下载0
收藏0
其他版本

除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。