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Deng X, Li W, Yang Y, Wang S, Zeng N, Xu J, Hassan H, Chen Z, Liu Y, Miao X, Guo Y, Chen R, Kang Y. COPD stage detection: leveraging the auto-metric graph neural network with inspiratory and expiratory chest CT images. Med Biol Eng Comput 2024; 62:1733-1749. [PMID: 38363487 DOI: 10.1007/s11517-024-03016-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Accepted: 12/30/2023] [Indexed: 02/17/2024]
Abstract
Chronic obstructive pulmonary disease (COPD) is a common lung disease that can lead to restricted airflow and respiratory problems, causing a significant health, economic, and social burden. Detecting the COPD stage can provide a timely warning for prompt intervention in COPD patients. However, existing methods based on inspiratory (IN) and expiratory (EX) chest CT images are not sufficiently accurate and efficient in COPD stage detection. The lung region images are autonomously segmented from IN and EX chest CT images to extract the 1 , 781 × 2 lung radiomics and 13 , 824 × 2 3D CNN features. Furthermore, a strategy for concatenating and selecting features was employed in COPD stage detection based on radiomics and 3D CNN features. Finally, we combine all the radiomics, 3D CNN features, and factor risks (age, gender, and smoking history) to detect the COPD stage based on the Auto-Metric Graph Neural Network (AMGNN). The AMGNN with radiomics and 3D CNN features achieves the best performance at 89.7 % of accuracy, 90.9 % of precision, 89.5 % of F1-score, and 95.8 % of AUC compared to six classic machine learning (ML) classifiers. Our proposed approach demonstrates high accuracy in detecting the stage of COPD using both IN and EX chest CT images. This method can potentially establish an efficient diagnostic tool for patients with COPD. Additionally, we have identified radiomics and 3D CNN as more appropriate biomarkers than Parametric Response Mapping (PRM). Moreover, our findings indicate that expiration yields better results than inspiration in detecting the stage of COPD.
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Affiliation(s)
- Xingguang Deng
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, 110169, China
- College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen, 518118, China
| | - Wei Li
- College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen, 518118, China
| | - Yingjian Yang
- Department of radiology, Shenzhen Lanmage Medical Technology Co., Ltd, No.103, Baguang Service Center, Shenzhen, Guangdong, 518119, People's Republic of China
| | - Shicong Wang
- College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen, 518118, China
- School of Applied Technology, Shenzhen University, Shenzhen, 518060, China
| | - Nanrong Zeng
- College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen, 518118, China
- School of Applied Technology, Shenzhen University, Shenzhen, 518060, China
| | - Jiaxuan Xu
- The First Affiliated Hospital of Guangzhou Medical University, State Key Laboratory of Respiratory Disease, Nation Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, The National Center for Respiratory Medicine, Guangzhou, 510120, China
| | - Haseeb Hassan
- College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen, 518118, China
| | - Ziran Chen
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, 110169, China
- College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen, 518118, China
| | - Yang Liu
- College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen, 518118, China
| | - Xiaoqiang Miao
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, 110169, China
- College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen, 518118, China
| | - Yingwei Guo
- School of Electrical and Information Engineering, Northeast Petroleum University, Daqing, 163318, China
| | - Rongchang Chen
- Department of Respiratory and Critical Care Medicine, First Affiliated Hospital of Southern University of Science and Technology, Second Clinical Medical College of Jinan University, Shenzhen People's Hospital, Shenzhen Institute of Respiratory Disease, Shenzhen, 518001, China.
| | - Yan Kang
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, 110169, China.
- College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen, 518118, China.
- Department of radiology, Shenzhen Lanmage Medical Technology Co., Ltd, No.103, Baguang Service Center, Shenzhen, Guangdong, 518119, People's Republic of China.
- Engineering Research Centre of Medical Imaging and Intelligent Analysis, Ministry of Education, Shenyang, 110169, China.
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Wang S, Li W, Zeng N, Xu J, Yang Y, Deng X, Chen Z, Duan W, Liu Y, Guo Y, Chen R, Kang Y. Acute exacerbation prediction of COPD based on Auto-metric graph neural network with inspiratory and expiratory chest CT images. Heliyon 2024; 10:e28724. [PMID: 38601695 PMCID: PMC11004525 DOI: 10.1016/j.heliyon.2024.e28724] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2023] [Revised: 03/16/2024] [Accepted: 03/22/2024] [Indexed: 04/12/2024] Open
Abstract
Chronic obstructive pulmonary disease (COPD) is a widely prevalent disease with significant mortality and disability rates and has become the third leading cause of death globally. Patients with acute exacerbation of COPD (AECOPD) often substantially suffer deterioration and death. Therefore, COPD patients deserve special consideration regarding treatment in this fragile population for pre-clinical health management. Based on the above, this paper proposes an AECOPD prediction model based on the Auto-Metric Graph Neural Network (AMGNN) using inspiratory and expiratory chest low-dose CT images. This study was approved by the ethics committee in the First Affiliated Hospital of Guangzhou Medical University. Subsequently, 202 COPD patients with inspiratory and expiratory chest CT Images and their annual number of AECOPD were collected after the exclusion. First, the inspiratory and expiratory lung parenchyma images of the 202 COPD patients are extracted using a trained ResU-Net. Then, inspiratory and expiratory lung Radiomics and CNN features are extracted from the 202 inspiratory and expiratory lung parenchyma images by Pyradiomics and pre-trained Med3D (a heterogeneous 3D network), respectively. Last, Radiomics and CNN features are combined and then further selected by the Lasso algorithm and generalized linear model for determining node features and risk factors of AMGNN, and then the AECOPD prediction model is established. Compared to related models, the proposed model performs best, achieving an accuracy of 0.944, precision of 0.950, F1-score of 0.944, ad area under the curve of 0.965. Therefore, it is concluded that our model may become an effective tool for AECOPD prediction.
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Affiliation(s)
- Shicong Wang
- College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen 518118, China
- School of Applied Technology, Shenzhen University, Shenzhen 518060, China
| | - Wei Li
- College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen 518118, China
| | - Nanrong Zeng
- College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen 518118, China
- School of Applied Technology, Shenzhen University, Shenzhen 518060, China
| | - Jiaxuan Xu
- The First Affiliated Hospital of Guangzhou Medical University, State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, The National Center for Respiratory Medicine, Guangzhou 510120, China
| | - Yingjian Yang
- College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen 518118, China
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang 110169, China
| | - Xingguang Deng
- College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen 518118, China
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang 110169, China
| | - Ziran Chen
- College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen 518118, China
| | - Wenxin Duan
- College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen 518118, China
- School of Applied Technology, Shenzhen University, Shenzhen 518060, China
| | - Yang Liu
- College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen 518118, China
| | - Yingwei Guo
- College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen 518118, China
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang 110169, China
| | - Rongchang Chen
- The First Affiliated Hospital of Guangzhou Medical University, State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, The National Center for Respiratory Medicine, Guangzhou 510120, China
- Department of Respiratory and Critical Care Medicine, First Affiliated Hospital of Southern University of Science and Technology, Second Clinical Medical College of Jinan University, Shenzhen People's Hospital, Shenzhen Institute of Respiratory Diseases, Shenzhen 518001, China
| | - Yan Kang
- College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen 518118, China
- School of Applied Technology, Shenzhen University, Shenzhen 518060, China
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang 110169, China
- Engineering Research Centre of Medical Imaging and Intelligent Analysis, Ministry of Education, Shenyang 110169, China
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Liu Y, Wu J, Zhou J, Guo J, Liang C, Xing Y, Wang Z, Chen L, Ding Y, Ren D, Bai Y, Hu D. Identification of high-risk population of pneumoconiosis using deep learning segmentation of lung 3D images and radiomics texture analysis. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 244:108006. [PMID: 38215580 DOI: 10.1016/j.cmpb.2024.108006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/19/2023] [Revised: 12/25/2023] [Accepted: 01/01/2024] [Indexed: 01/14/2024]
Abstract
OBJECTION The aim of this study is to develop an early-warning model for identifying high-risk populations of pneumoconiosis by combining lung 3D images and radiomics lung texture features. METHODS A retrospective study was conducted, including 600 dust-exposed workers and 300 confirmed pneumoconiosis patients. Chest computed tomography (CT) images were divided into a training set and a test set in a 2:1 ratio. Whole-lung segmentation was performed using deep learning models for feature extraction of radiomics. Two feature selection algorithms and five classification models were used. The optimal model was selected using a 10-fold cross-validation strategy, and the calibration curve and decision curve were evaluated. To verify the applicability of the model, the diagnostic efficiency and accuracy between the model and human interpretation were compared. Additionally, the risk probabilities for different risk groups defined by the model were compared at different time intervals. RESULTS Four radiomics features were ultimately used to construct the predictive model. The logistic regression model was the most stable in both the training set and testing set, with an area under curve (AUC) of 0.964 (95 % confidence interval [CI], 0.950-0.976) and 0.947 (95 %CI, 0.925-0.964). In the training and testing sets, the Brier scores were 0.092 and 0.14, respectively, with threshold probability ranges of 2 %-99 % and 2 %-85 %. These results indicate that the model exhibits good calibration and clinical benefit. The comparison between the model and human interpretation showed that the model was not inferior in terms of diagnostic efficiency and accuracy. Additionally, the high-risk population identified by the model was diagnosed as pneumoconiosis two years later. CONCLUSION This study provides a meticulous and quantifiable method for detecting and assessing the risk of pneumoconiosis, building upon accurate diagnosis. Employing risk scoring and probability estimation, not only enhances the efficiency of diagnostic physicians but also provides a valuable reference for controlling the occurrence of pneumoconiosis.
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Affiliation(s)
- Yafeng Liu
- School of Medicine, Anhui University of Science and Technology, Huainan, PR China; Anhui Province Engineering Laboratory of Occupational Health and Safety, Anhui University of Science and Technology, Huainan, PR China
| | - Jing Wu
- School of Medicine, Anhui University of Science and Technology, Huainan, PR China; Anhui Province Engineering Laboratory of Occupational Health and Safety, Anhui University of Science and Technology, Huainan, PR China.
| | - Jiawei Zhou
- School of Medicine, Anhui University of Science and Technology, Huainan, PR China; Anhui Province Engineering Laboratory of Occupational Health and Safety, Anhui University of Science and Technology, Huainan, PR China
| | - Jianqiang Guo
- School of Medicine, Anhui University of Science and Technology, Huainan, PR China; Anhui Province Engineering Laboratory of Occupational Health and Safety, Anhui University of Science and Technology, Huainan, PR China
| | - Chao Liang
- School of Medicine, Anhui University of Science and Technology, Huainan, PR China; Anhui Province Engineering Laboratory of Occupational Health and Safety, Anhui University of Science and Technology, Huainan, PR China
| | - Yingru Xing
- School of Medicine, Anhui University of Science and Technology, Huainan, PR China; Department of Clinical Laboratory, Anhui Zhongke Gengjiu Hospital, Hefei, PR China
| | - Zhongyu Wang
- Ziwei King Star Digital Technology Co., Ltd., Hefei, PR China
| | - Lijuan Chen
- Occupational Control Hospital of Huaihe Energy Group, Huainan, PR China
| | - Yan Ding
- Occupational Control Hospital of Huaihe Energy Group, Huainan, PR China
| | - Dingfei Ren
- Occupational Control Hospital of Huaihe Energy Group, Huainan, PR China.
| | - Ying Bai
- School of Medicine, Anhui University of Science and Technology, Huainan, PR China; Anhui Province Engineering Laboratory of Occupational Health and Safety, Anhui University of Science and Technology, Huainan, PR China
| | - Dong Hu
- School of Medicine, Anhui University of Science and Technology, Huainan, PR China; Anhui Province Engineering Laboratory of Occupational Health and Safety, Anhui University of Science and Technology, Huainan, PR China; Key Laboratory of Industrial Dust Prevention and Control & Occupational Safety and Health of the Ministry of Education, Anhui University of Science and Technology, Huainan, PR China.
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