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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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Chen X, Wang X, Huang S, Luo W, Luo Z, Chen Z. Study on Predicting Clinical Stage of Patients with Bronchial Asthma Based on CT Radiomics. J Asthma Allergy 2024; 17:291-303. [PMID: 38562252 PMCID: PMC10982665 DOI: 10.2147/jaa.s448064] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2023] [Accepted: 03/21/2024] [Indexed: 04/04/2024] Open
Abstract
Objective To explore the value of a new model based on CT radiomics in predicting the staging of patients with bronchial asthma (BA). Methods Patients with BA from 2018 to 2021 were retrospectively analyzed and underwent plain chest CT before treatment. According to the guidelines for the prevention and treatment of BA (2016 edition), they were divided into two groups: acute attack and non-acute attack. The images were processed as follows: using Lung Kit software for image standardization and segmentation, using AK software for image feature extraction, and using R language for data analysis and model construction (training set: test set = 7: 3). The efficacy and clinical effects of the constructed model were evaluated with ROC curve, sensitivity, specificity, calibration curve and decision curve. Results A total of 112 patients with BA were enrolled, including 80 patients with acute attack (range: 2-86 years old, mean: 53.89±17.306 years old, males of 33) and 32 patients with non-acute attack (range: 4-79 years old, mean: 57.38±19.223 years old, males of 18). A total of 10 imaging features are finally retained and used to construct model using multi-factor logical regression method. In the training group, the AUC, sensitivity and specificity of the model was 0.881 (95% CI:0.808-0.955), 0.804 and 0.818, separately; while in the test group, it was 0.792 (95% CI:0.608-0.976), 0.792 and 0.80, respectively. Conclusion The model constructed based on radiomics has a good effect on predicting the staging of patients with BA, which provides a new method for clinical diagnosis of staging in BA patients.
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Affiliation(s)
- Xiaodong Chen
- Radiology Imaging Center, Affiliated Hospital of Guangdong Medical University, Zhanjiang City, People’s Republic of China
| | - Xiangyuan Wang
- Radiology Imaging Center, Affiliated Hospital of Guangdong Medical University, Zhanjiang City, People’s Republic of China
| | - Shangqing Huang
- Radiology Imaging Center, Affiliated Hospital of Guangdong Medical University, Zhanjiang City, People’s Republic of China
| | - Wenxuan Luo
- Radiology Imaging Center, Affiliated Hospital of Guangdong Medical University, Zhanjiang City, People’s Republic of China
| | - Zebin Luo
- Radiology Imaging Center, Affiliated Hospital of Guangdong Medical University, Zhanjiang City, People’s Republic of China
| | - Zipan Chen
- Health Management Center, Affiliated Hospital of Guangdong Medical University, Zhanjiang City, People’s Republic of China
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Zhou TH, Zhou XX, Ni J, Ma YQ, Xu FY, Fan B, Guan Y, Jiang XA, Lin XQ, Li J, Xia Y, Wang X, Wang Y, Huang WJ, Tu WT, Dong P, Li ZB, Liu SY, Fan L. CT whole lung radiomic nomogram: a potential biomarker for lung function evaluation and identification of COPD. Mil Med Res 2024; 11:14. [PMID: 38374260 PMCID: PMC10877876 DOI: 10.1186/s40779-024-00516-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/04/2023] [Accepted: 01/22/2024] [Indexed: 02/21/2024] Open
Abstract
BACKGROUND Computed tomography (CT) plays a great role in characterizing and quantifying changes in lung structure and function of chronic obstructive pulmonary disease (COPD). This study aimed to explore the performance of CT-based whole lung radiomic in discriminating COPD patients and non-COPD patients. METHODS This retrospective study was performed on 2785 patients who underwent pulmonary function examination in 5 hospitals and were divided into non-COPD group and COPD group. The radiomic features of the whole lung volume were extracted. Least absolute shrinkage and selection operator (LASSO) logistic regression was applied for feature selection and radiomic signature construction. A radiomic nomogram was established by combining the radiomic score and clinical factors. Receiver operating characteristic (ROC) curve analysis and decision curve analysis (DCA) were used to evaluate the predictive performance of the radiomic nomogram in the training, internal validation, and independent external validation cohorts. RESULTS Eighteen radiomic features were collected from the whole lung volume to construct a radiomic model. The area under the curve (AUC) of the radiomic model in the training, internal, and independent external validation cohorts were 0.888 [95% confidence interval (CI) 0.869-0.906], 0.874 (95%CI 0.844-0.904) and 0.846 (95%CI 0.822-0.870), respectively. All were higher than the clinical model (AUC were 0.732, 0.714, and 0.777, respectively, P < 0.001). DCA demonstrated that the nomogram constructed by combining radiomic score, age, sex, height, and smoking status was superior to the clinical factor model. CONCLUSIONS The intuitive nomogram constructed by CT-based whole-lung radiomic has shown good performance and high accuracy in identifying COPD in this multicenter study.
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Affiliation(s)
- Tao-Hu Zhou
- Department of Radiology, the Second Affiliated Hospital of Naval Medical University, Shanghai, 200003, China
- School of Medical Imaging, Shandong Second Medical University, Weifang, 261053, Shandong, China
| | - Xiu-Xiu Zhou
- Department of Radiology, the Second Affiliated Hospital of Naval Medical University, Shanghai, 200003, China
| | - Jiong Ni
- Department of Radiology, School of Medicine, Tongji Hospital, Tongji University, Shanghai, 200065, China
| | - Yan-Qing Ma
- Department of Radiology, Zhejiang Province People's Hospital, Affiliated People's Hospital of Hangzhou Medical College, Hangzhou, 310014, China
| | - Fang-Yi Xu
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang, 310018, China
| | - Bing Fan
- Jiangxi Provincial People's Hospital, the First Affiliated Hospital of Nanchang Medical College, Nanchang, 330006, China
| | - Yu Guan
- Department of Radiology, the Second Affiliated Hospital of Naval Medical University, Shanghai, 200003, China
| | - Xin-Ang Jiang
- Department of Radiology, the Second Affiliated Hospital of Naval Medical University, Shanghai, 200003, China
| | - Xiao-Qing Lin
- Department of Radiology, the Second Affiliated Hospital of Naval Medical University, Shanghai, 200003, China
- College of Health Sciences and Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China
| | - Jie Li
- Department of Radiology, the Second Affiliated Hospital of Naval Medical University, Shanghai, 200003, China
- College of Health Sciences and Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China
| | - Yi Xia
- Department of Radiology, the Second Affiliated Hospital of Naval Medical University, Shanghai, 200003, China
| | - Xiang Wang
- Department of Radiology, the Second Affiliated Hospital of Naval Medical University, Shanghai, 200003, China
| | - Yun Wang
- Department of Radiology, the Second Affiliated Hospital of Naval Medical University, Shanghai, 200003, China
| | - Wen-Jun Huang
- Department of Radiology, the Second Affiliated Hospital of Naval Medical University, Shanghai, 200003, China
- Department of Radiology, the Second People's Hospital of Deyang, Deyang, 618000, Sichuan, China
| | - Wen-Ting Tu
- Department of Radiology, the Second Affiliated Hospital of Naval Medical University, Shanghai, 200003, China
| | - Peng Dong
- School of Medical Imaging, Shandong Second Medical University, Weifang, 261053, Shandong, China
| | - Zhao-Bin Li
- Department of Radiation Oncology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, 200233, China
| | - Shi-Yuan Liu
- Department of Radiology, the Second Affiliated Hospital of Naval Medical University, Shanghai, 200003, China
| | - Li Fan
- Department of Radiology, the Second Affiliated Hospital of Naval Medical University, Shanghai, 200003, China.
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McCague C, Ramlee S, Reinius M, Selby I, Hulse D, Piyatissa P, Bura V, Crispin-Ortuzar M, Sala E, Woitek R. Introduction to radiomics for a clinical audience. Clin Radiol 2023; 78:83-98. [PMID: 36639175 DOI: 10.1016/j.crad.2022.08.149] [Citation(s) in RCA: 16] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Accepted: 08/31/2022] [Indexed: 01/12/2023]
Abstract
Radiomics is a rapidly developing field of research focused on the extraction of quantitative features from medical images, thus converting these digital images into minable, high-dimensional data, which offer unique biological information that can enhance our understanding of disease processes and provide clinical decision support. To date, most radiomics research has been focused on oncological applications; however, it is increasingly being used in a raft of other diseases. This review gives an overview of radiomics for a clinical audience, including the radiomics pipeline and the common pitfalls associated with each stage. Key studies in oncology are presented with a focus on both those that use radiomics analysis alone and those that integrate its use with other multimodal data streams. Importantly, clinical applications outside oncology are also presented. Finally, we conclude by offering a vision for radiomics research in the future, including how it might impact our practice as radiologists.
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Affiliation(s)
- C McCague
- Department of Radiology, University of Cambridge, Cambridge, UK; Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, UK; Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK; Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK.
| | - S Ramlee
- Department of Radiology, University of Cambridge, Cambridge, UK
| | - M Reinius
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, UK; Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK; Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
| | - I Selby
- Department of Radiology, University of Cambridge, Cambridge, UK; Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
| | - D Hulse
- Department of Radiology, University of Cambridge, Cambridge, UK; Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
| | - P Piyatissa
- Department of Radiology, University of Cambridge, Cambridge, UK
| | - V Bura
- Department of Radiology, University of Cambridge, Cambridge, UK; Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK; Department of Radiology and Medical Imaging, County Clinical Emergency Hospital, Cluj-Napoca, Romania
| | - M Crispin-Ortuzar
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, UK; Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK; Department of Oncology, University of Cambridge, Cambridge, UK
| | - E Sala
- Department of Radiology, University of Cambridge, Cambridge, UK; Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, UK; Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
| | - R Woitek
- Department of Radiology, University of Cambridge, Cambridge, UK; Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, UK; Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK; Research Centre for Medical Image Analysis and Artificial Intelligence (MIAAI), Department of Medicine, Faculty of Medicine and Dentistry, Danube Private University, Krems, Austria
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Yang Y, Chen Z, Li W, Zeng N, Guo Y, Wang S, Duan W, Liu Y, Chen H, Li X, Chen R, Kang Y. Multi-modal data combination strategy based on chest HRCT images and PFT parameters for intelligent dyspnea identification in COPD. Front Med (Lausanne) 2022; 9:980950. [PMID: 36619622 PMCID: PMC9811121 DOI: 10.3389/fmed.2022.980950] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Accepted: 12/06/2022] [Indexed: 12/24/2022] Open
Abstract
Introduction Because of persistent airflow limitation in chronic obstructive pulmonary disease (COPD), patients with COPD often have complications of dyspnea. However, as a leading symptom of COPD, dyspnea in COPD deserves special consideration regarding treatment in this fragile population for pre-clinical health management in COPD. Methods: Based on the above, this paper proposes a multi-modal data combination strategy by combining the local and global features for dyspnea identification in COPD based on the multi-layer perceptron (MLP) classifier. Methods First, lung region images are automatically segmented from chest HRCT images for extracting the original 1,316 lung radiomics (OLR, 1,316) and 13,824 3D CNN features (O3C, 13,824). Second, the local features, including five selected pulmonary function test (PFT) parameters (SLF, 5), 28 selected lung radiomics (SLR, 28), and 22 selected 3D CNN features (S3C, 22), are respectively selected from the original 11 PFT parameters (OLF, 11), 1,316 OLR, and 13,824 O3C by the least absolute shrinkage and selection operator (Lasso) algorithm. Meantime, the global features, including two fused PFT parameters (FLF, 2), six fused lung radiomics (FLR, 6), and 34 fused 3D CNN features (F3C, 34), are respectively fused by 11 OLF, 1,316 OLR, and 13,824 O3C using the principal component analysis (PCA) algorithm. Finally, we combine all the local and global features (SLF + FLF + SLR + FLR + S3C + F3C, 5+ 2 + 28 + 6 + 22 + 34) for dyspnea identification in COPD based on the MLP classifier. Results Our proposed method comprehensively improves classification performance. The MLP classifier with all the local and global features achieves the best classification performance at 87.7% of accuracy, 87.7% of precision, 87.7% of recall, 87.7% of F1-scorel, and 89.3% of AUC, respectively. Discussion Compared with single-modal data, the proposed strategy effectively improves the classification performance for dyspnea identification in COPD, providing an objective and effective tool for COPD management.
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Affiliation(s)
- Yingjian Yang
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China,College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen, China
| | - Ziran Chen
- College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen, China
| | - Wei Li
- College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen, China
| | - Nanrong Zeng
- College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen, China,School of Applied Technology, Shenzhen University, Shenzhen, China
| | - Yingwei Guo
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China,College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen, China
| | - Shicong Wang
- College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen, China,School of Applied Technology, Shenzhen University, Shenzhen, China
| | - Wenxin Duan
- College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen, China,School of Applied Technology, Shenzhen University, Shenzhen, China
| | - Yang Liu
- College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen, China,School of Applied Technology, Shenzhen University, Shenzhen, China
| | - Huai Chen
- Department of Radiology, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Xian Li
- Department of Radiology, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Rongchang Chen
- Shenzhen Institute of Respiratory Diseases, Shenzhen People's Hospital, Shenzhen, China,The Second Clinical Medical College, Jinan University, Guangzhou, China,The First Affiliated Hospital, Southern University of Science and Technology, Shenzhen, China,Rongchang Chen ✉
| | - Yan Kang
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China,College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen, China,School of Applied Technology, Shenzhen University, Shenzhen, China,Engineering Research Centre of Medical Imaging and Intelligent Analysis, Ministry of Education, Shenyang, China,*Correspondence: Yan Kang ✉
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Lung Radiomics Features Selection for COPD Stage Classification Based on Auto-Metric Graph Neural Network. Diagnostics (Basel) 2022; 12:diagnostics12102274. [PMID: 36291964 PMCID: PMC9600898 DOI: 10.3390/diagnostics12102274] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2022] [Revised: 09/13/2022] [Accepted: 09/18/2022] [Indexed: 11/17/2022] Open
Abstract
Chronic obstructive pulmonary disease (COPD) is a preventable, treatable, progressive chronic disease characterized by persistent airflow limitation. Patients with COPD deserve special consideration regarding treatment in this fragile population for preclinical health management. Therefore, this paper proposes a novel lung radiomics combination vector generated by a generalized linear model (GLM) and Lasso algorithm for COPD stage classification based on an auto-metric graph neural network (AMGNN) with a meta-learning strategy. Firstly, the parenchyma images were segmented from chest high-resolution computed tomography (HRCT) images by ResU-Net. Second, lung radiomics features are extracted from the parenchyma images by PyRadiomics. Third, a novel lung radiomics combination vector (3 + 106) is constructed by the GLM and Lasso algorithm for determining the radiomics risk factors (K = 3) and radiomics node features (d = 106). Last, the COPD stage is classified based on the AMGNN. The results show that compared with the convolutional neural networks and machine learning models, the AMGNN based on constructed novel lung radiomics combination vector performs best, achieving an accuracy of 0.943, precision of 0.946, recall of 0.943, F1-score of 0.943, and ACU of 0.984. Furthermore, it is found that our method is effective for COPD stage classification.
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