1
|
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.
Collapse
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.
| |
Collapse
|
2
|
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.
Collapse
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
| |
Collapse
|
3
|
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.
Collapse
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.
| |
Collapse
|
4
|
Lee JH, Kim S, Kim YJ, Lee SW, Lee JS, Oh YM. COPD Risk Factor Profiles in General Population and Referred Patients: Potential Etiotypes. Int J Chron Obstruct Pulmon Dis 2023; 18:2509-2520. [PMID: 37965078 PMCID: PMC10642581 DOI: 10.2147/copd.s427774] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2023] [Accepted: 11/01/2023] [Indexed: 11/16/2023] Open
Abstract
Purpose To identify the risk factors for chronic obstructive pulmonary disease (COPD) in view of potential etiotypes in a general population and referred COPD patients. Patients and Methods We performed a cross-sectional observational study utilizing two distinct datasets: a dataset of a general population including 2430 subjects with COPD from the Korea National Health and Nutrition Examination Survey (KNHANES) and another dataset of referral clinics including 579 patients with COPD from the Korean Obstructive Lung Disease (KOLD). Results The mean age of both groups was 67 years, and 71.2% and 93.8% were male in the COPD subjects from the KNHANES and the KOLD, respectively. The mean forced expiratory volume in 1 second of predicted value was 79.1% (KNHANES) and 55.4% (KOLD). The frequency of risk factors of cigarette smoking (C), infection (I), pollution (P), and asthma (A) was 54.6%, 9.4%, 10.7%, and 7.9%, respectively, in the KNHANES COPD subjects, and 88.4%, 26.6%, 41.6%, and 35.2%, respectively, in the KOLD COPD subjects. Risk factors were unidentified in 32.6% (KNHANES) and 3.1% (KOLD) of COPD subjects. Additionally, 14.1% and 66.2% of subjects with COPD had two or more risk factors in the KNHANES and KOLD, respectively. Conclusion The profiles of risk factors C, I, P, and A were identified and appeared to be different among the two COPD groups from a general population or referral clinics. In some of the COPD subjects, risk factors were not identified, so we should endeavour to find out unidentified COPD risk factors, especially in the general population.
Collapse
Affiliation(s)
- Jang Ho Lee
- Department of Pulmonary and Critical Care Medicine, Asan Medical Centre, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Sehee Kim
- Department of Clinical Epidemiology and Biostatistics, Asan Medical Centre, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Ye-Jee Kim
- Department of Clinical Epidemiology and Biostatistics, Asan Medical Centre, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Sei Won Lee
- Department of Pulmonary and Critical Care Medicine, Asan Medical Centre, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Jae Seung Lee
- Department of Pulmonary and Critical Care Medicine, Asan Medical Centre, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Yeon-Mok Oh
- Department of Pulmonary and Critical Care Medicine, Asan Medical Centre, University of Ulsan College of Medicine, Seoul, Republic of Korea
| |
Collapse
|
5
|
Yu X, Kang B, Nie P, Deng Y, Liu Z, Mao N, An Y, Xu J, Huang C, Huang Y, Zhang Y, Hou Y, Zhang L, Sun Z, Zhu B, Shi R, Zhang S, Sun C, Wang X. Development and validation of a CT-based radiomics model for differentiating pneumonia-like primary pulmonary lymphoma from infectious pneumonia: A multicenter study. Chin Med J (Engl) 2023; 136:1188-1197. [PMID: 37083119 PMCID: PMC10278712 DOI: 10.1097/cm9.0000000000002671] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2022] [Indexed: 04/22/2023] Open
Abstract
BACKGROUND Pneumonia-like primary pulmonary lymphoma (PPL) was commonly misdiagnosed as infectious pneumonia, leading to delayed treatment. The purpose of this study was to establish a computed tomography (CT)-based radiomics model to differentiate pneumonia-like PPL from infectious pneumonia. METHODS In this retrospective study, 79 patients with pneumonia-like PPL and 176 patients with infectious pneumonia from 12 medical centers were enrolled. Patients from center 1 to center 7 were assigned to the training or validation cohort, and the remaining patients from other centers were used as the external test cohort. Radiomics features were extracted from CT images. A three-step procedure was applied for radiomics feature selection and radiomics signature building, including the inter- and intra-class correlation coefficients (ICCs), a one-way analysis of variance (ANOVA), and least absolute shrinkage and selection operator (LASSO). Univariate and multivariate analyses were used to identify the significant clinicoradiological variables and construct a clinical factor model. Two radiologists reviewed the CT images for the external test set. Performance of the radiomics model, clinical factor model, and each radiologist were assessed by receiver operating characteristic, and area under the curve (AUC) was compared. RESULTS A total of 144 patients (44 with pneumonia-like PPL and 100 infectious pneumonia) were in the training cohort, 38 patients (12 with pneumonia-like PPL and 26 infectious pneumonia) were in the validation cohort, and 73 patients (23 with pneumonia-like PPL and 50 infectious pneumonia) were in the external test cohort. Twenty-three radiomics features were selected to build the radiomics model, which yielded AUCs of 0.95 (95% confidence interval [CI]: 0.94-0.99), 0.93 (95% CI: 0.85-0.98), and 0.94 (95% CI: 0.87-0.99) in the training, validation, and external test cohort, respectively. The AUCs for the two readers and clinical factor model were 0.74 (95% CI: 0.63-0.83), 0.72 (95% CI: 0.62-0.82), and 0.73 (95% CI: 0.62-0.84) in the external test cohort, respectively. The radiomics model outperformed both the readers' interpretation and clinical factor model ( P <0.05). CONCLUSIONS The CT-based radiomics model may provide an effective and non-invasive tool to differentiate pneumonia-like PPL from infectious pneumonia, which might provide assistance for clinicians in tailoring precise therapy.
Collapse
Affiliation(s)
- Xinxin Yu
- Department of Radiology, Shandong Provincial Hospital, Shandong University, Jinan, Shandong 250021, China
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong 250021, China
| | - Bing Kang
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong 250021, China
| | - Pei Nie
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, Shandong 266000, China
| | - Yan Deng
- Department of Radiology, Qilu Hospital, Shandong University, Jinan, Shandong 250012, China
| | - Zixin Liu
- Department of Medicine, Graduate School, Kyung Hee University, Seoul 446701, Republic of Korea
| | - Ning Mao
- Department of Radiology, Yantai Yuhuangding Hospital, Affiliated Hospital of Qingdao University, Yantai, Shandong 164000, China
| | - Yahui An
- Department of Research Collaboration, R&D Center, Beijing Deepwise & League of PHD Technology Co., Ltd, Beijing 100080, China
| | - Jingxu Xu
- Department of Research Collaboration, R&D Center, Beijing Deepwise & League of PHD Technology Co., Ltd, Beijing 100080, China
| | - Chencui Huang
- Department of Research Collaboration, R&D Center, Beijing Deepwise & League of PHD Technology Co., Ltd, Beijing 100080, China
| | - Yong Huang
- Department of Radiology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong 250117, China
| | - Yonggao Zhang
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan 450052, China
| | - Yang Hou
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, Liaoning 110004, China
| | - Longjiang Zhang
- Department of Medical Imaging, Jinling Hospital, Medical School of Nanjing University, Nanjing, Jiangsu 210002, China
| | - Zhanguo Sun
- Department of Radiology, Affiliated Hospital of Jining Medical University, Jining, Shandong 272029, China
| | - Baosen Zhu
- Department of Radiology, Shandong Provincial Hospital, Shandong University, Jinan, Shandong 250021, China
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong 250021, China
| | - Rongchao Shi
- Department of Radiology, Shandong Provincial Hospital, Shandong University, Jinan, Shandong 250021, China
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong 250021, China
| | - Shuai Zhang
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong 250021, China
| | - Cong Sun
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong 250021, China
| | - Ximing Wang
- Department of Radiology, Shandong Provincial Hospital, Shandong University, Jinan, Shandong 250021, China
| |
Collapse
|
6
|
Seo JB. Computerized Tomographic Assessment for Phenotyping Asthma. ALLERGY, ASTHMA & IMMUNOLOGY RESEARCH 2023; 15:122-124. [PMID: 37021500 PMCID: PMC10079513 DOI: 10.4168/aair.2023.15.2.122] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/10/2023] [Revised: 01/25/2023] [Accepted: 01/31/2023] [Indexed: 04/07/2023]
Affiliation(s)
- Joon Beom Seo
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.
| |
Collapse
|
7
|
Yang Y, Wang S, Zeng N, Duan W, Chen Z, Liu Y, Li W, Guo Y, Chen H, Li X, Chen R, Kang Y. Lung Radiomics Features Selection for COPD Stage Classification Based on Auto-Metric Graph Neural Network. Diagnostics (Basel) 2022; 12:2274. [PMID: 36291964 PMCID: PMC9600898 DOI: 10.3390/diagnostics12102274] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [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.
Collapse
Affiliation(s)
- Yingjian Yang
- 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
| | - 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
| | - Wenxin Duan
- College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen 518118, China
- School of Applied Technology, Shenzhen University, Shenzhen 518060, China
| | - Ziran Chen
- 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
| | - Wei Li
- College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen 518118, China
| | - Yingwei Guo
- 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
| | - Huai Chen
- Department of Radiology, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou 510120, China
| | - Xian Li
- Department of Radiology, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou 510120, China
| | - Rongchang Chen
- Shenzhen Institute of Respiratory Diseases, Shenzhen People’s Hospital, Shenzhen 518001, China
- The Second Clinical Medical College, Jinan University, Guangzhou 518001, China
- The First Affiliated Hospital, Southern University of Science and Technology, 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
- School of Applied Technology, Shenzhen University, Shenzhen 518060, China
- Engineering Research Centre of Medical Imaging and Intelligent Analysis, Ministry of Education, Shenyang 110169, China
| |
Collapse
|
8
|
Cui S, Shu Z, Ma Y, Lin Y, Wang H, Cao H, Liu J, Gong X. A novel computed tomography radiomic nomogram for early evaluation of small airway dysfunction development. Front Med (Lausanne) 2022; 9:944294. [PMID: 36177331 PMCID: PMC9513435 DOI: 10.3389/fmed.2022.944294] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2022] [Accepted: 08/15/2022] [Indexed: 11/25/2022] Open
Abstract
The common respiratory abnormality, small airway dysfunction (fSAD), is easily neglected. Its prognostic factors, prevalence, and risk factors are unclear. This study aimed to explore the early detection of fSAD using radiomic analysis of computed tomography (CT) images to predict fSAD progress. The patients were divided into fSAD and non-fSAD groups and divided randomly into a training group (n = 190) and a validation group (n = 82) at a 7:3 ratio. Lung kit software was used for automatic delineation of regions of interest (ROI) on chest CT images. The most valuable imaging features were selected and a radiomic score was established for risk assessment. Multivariate logistic regression analysis showed that age, radiomic score, smoking, and history of asthma were significant predictors of fSAD (P < 0.05). Results suggested that the radiomic nomogram model provides clinicians with useful data and could represent a reliable reference to form fSAD clinical treatment strategies.
Collapse
Affiliation(s)
- Sijia Cui
- Rehabilitation Medicine Center, Department of Radiology, Zhejiang Provincial People’s Hospital, Affiliated People’s Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, China
| | - Zhenyu Shu
- Rehabilitation Medicine Center, Department of Radiology, Zhejiang Provincial People’s Hospital, Affiliated People’s Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, China
| | - Yanqing Ma
- Rehabilitation Medicine Center, Department of Radiology, Zhejiang Provincial People’s Hospital, Affiliated People’s Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, China
| | - Yi Lin
- Rehabilitation Medicine Center, Department of Radiology, Zhejiang Provincial People’s Hospital, Affiliated People’s Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, China
| | - Haochu Wang
- Rehabilitation Medicine Center, Department of Radiology, Zhejiang Provincial People’s Hospital, Affiliated People’s Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, China
| | - Hanbo Cao
- Rehabilitation Medicine Center, Department of Radiology, Zhejiang Provincial People’s Hospital, Affiliated People’s Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, China
| | - Jing Liu
- Rehabilitation Medicine Center, Department of Radiology, Zhejiang Provincial People’s Hospital, Affiliated People’s Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, China
| | - Xiangyang Gong
- Rehabilitation Medicine Center, Department of Radiology, Zhejiang Provincial People’s Hospital, Affiliated People’s Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, China
- Hangzhou Medical College, Institute of Artificial Intelligence and Remote Imaging, Hangzhou, China
- *Correspondence: Xiangyang Gong,
| |
Collapse
|
9
|
Deep radiomics-based survival prediction in patients with chronic obstructive pulmonary disease. Sci Rep 2021; 11:15144. [PMID: 34312450 PMCID: PMC8313653 DOI: 10.1038/s41598-021-94535-4] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2020] [Accepted: 07/05/2021] [Indexed: 12/16/2022] Open
Abstract
Heterogeneous clinical manifestations and progression of chronic obstructive pulmonary disease (COPD) affect patient health risk assessment, stratification, and management. Pulmonary function tests are used to diagnose and classify the severity of COPD, but they cannot fully represent the type or range of pathophysiologic abnormalities of the disease. To evaluate whether deep radiomics from chest computed tomography (CT) images can predict mortality in patients with COPD, we designed a convolutional neural network (CNN) model for extracting representative features from CT images and then performed random survival forest to predict survival in COPD patients. We trained CNN-based binary classifier based on six-minute walk distance results (> 440 m or not) and extracted high-throughput image features (i.e., deep radiomics) directly from the last fully connected layer of it. The various sizes of fully connected layers and combinations of deep features were experimented using a discovery cohort with 344 patients from the Korean Obstructive Lung Disease cohort and an external validation cohort with 102 patients from Penang General Hospital in Malaysia. In the integrative analysis of discovery and external validation cohorts, with combining 256 deep features from the coronal slice of the vertebral body and two sagittal slices of the left/right lung, deep radiomics for survival prediction achieved concordance indices of 0.8008 (95% CI, 0.7642–0.8373) and 0.7156 (95% CI, 0.7024–0.7288), respectively. Deep radiomics from CT images could be used to predict mortality in COPD patients.
Collapse
|
10
|
Radiomics in Lung Diseases Imaging: State-of-the-Art for Clinicians. J Pers Med 2021; 11:jpm11070602. [PMID: 34202096 PMCID: PMC8306026 DOI: 10.3390/jpm11070602] [Citation(s) in RCA: 35] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2021] [Revised: 06/16/2021] [Accepted: 06/21/2021] [Indexed: 12/11/2022] Open
Abstract
Artificial intelligence (AI) has increasingly been serving the field of radiology over the last 50 years. As modern medicine is evolving towards precision medicine, offering personalized patient care and treatment, the requirement for robust imaging biomarkers has gradually increased. Radiomics, a specific method generating high-throughput extraction of a tremendous amount of quantitative imaging data using data-characterization algorithms, has shown great potential in individuating imaging biomarkers. Radiomic analysis can be implemented through the following two methods: hand-crafted radiomic features extraction or deep learning algorithm. Its application in lung diseases can be used in clinical decision support systems, regarding its ability to develop descriptive and predictive models in many respiratory pathologies. The aim of this article is to review the recent literature on the topic, and briefly summarize the interest of radiomics in chest Computed Tomography (CT) and its pertinence in the field of pulmonary diseases, from a clinician's perspective.
Collapse
|