1
|
Hang W, Bu C, Cui Y, Chen K, Zhang D, Li H, Wang S. Research progress on the pathogenesis and prediction of pneumoconiosis among coal miners. ENVIRONMENTAL GEOCHEMISTRY AND HEALTH 2024; 46:319. [PMID: 39012521 DOI: 10.1007/s10653-024-02114-z] [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: 04/13/2024] [Accepted: 07/02/2024] [Indexed: 07/17/2024]
Abstract
Pneumoconiosis is the most common occupational disease among coal miners, which is a lung disease caused by long-term inhalation of coal dust and retention in the lungs. The early stage of this disease is highly insidious, and pulmonary fibrosis may occur in the middle and late stages, leading to an increase in patient pain index and mortality rate. Currently, there is a lack of effective treatment methods. The pathogenesis of pneumoconiosis is complex and has many influencing factors. Although the characteristics of coal dust have been considered the main cause of different mechanisms of pneumoconiosis, the effects of coal dust composition, particle size and shape, and coal dust concentration on the pathogenesis of pneumoconiosis have not been systematically elucidated. Meanwhile, considering the irreversibility of pneumoconiosis progression, early prediction for pneumoconiosis patients is particularly important. However, there is no early prediction standard for pneumoconiosis among coal miners. This review summarizes the relevant research on the pathogenesis and prediction of pneumoconiosis in coal miners in recent years. Firstly, the pathogenesis of coal worker pneumoconiosis and silicosis was discussed, and the impact of coal dust characteristics on pneumoconiosis was analyzed. Then, the early diagnostic methods for pneumoconiosis have been systematically introduced, with a focus on image collaborative computer-aided diagnosis analysis and biomarker detection. Finally, the challenge of early screening technology for miners with pneumoconiosis was proposed.
Collapse
Affiliation(s)
- Wenlu Hang
- Department of Respiratory and Critical Care Medicine, Second Affiliated Hospital of Xuzhou Medical University, Xuzhou, 221000, Jiangsu Province, People's Republic of China
| | - Chunlu Bu
- Department of Respiratory and Critical Care Medicine, Second Affiliated Hospital of Xuzhou Medical University, Xuzhou, 221000, Jiangsu Province, People's Republic of China
| | - Yuming Cui
- School of Mechatronic Engineering, Jiangsu Normal University, Xuzhou, 221000, Jiangsu Province, People's Republic of China
| | - Kai Chen
- School of Materials Science and Physics, China University of Mining and Technology, Xuzhou, 221000, Jiangsu Province, People's Republic of China
| | - Dekun Zhang
- School of Materials Science and Physics, China University of Mining and Technology, Xuzhou, 221000, Jiangsu Province, People's Republic of China
| | - Haiquan Li
- Department of Respiratory and Critical Care Medicine, Second Affiliated Hospital of Xuzhou Medical University, Xuzhou, 221000, Jiangsu Province, People's Republic of China.
- School of Chemical Engineering & Technology, China University of Mining and Technology, Xuzhou, 221000, Jiangsu Province, People's Republic of China.
| | - Songquan Wang
- School of Mechatronic Engineering, Jiangsu Normal University, Xuzhou, 221000, Jiangsu Province, People's Republic of China.
| |
Collapse
|
2
|
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.
Collapse
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.
| |
Collapse
|
3
|
Gong X, Guo Y, Zhu T, Xing D, Shi Q, Zhang M. Noninvasive assessment of significant liver fibrosis in rabbits by spectral CT parameters and texture analysis. Jpn J Radiol 2023; 41:983-993. [PMID: 37071251 DOI: 10.1007/s11604-023-01423-0] [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: 01/20/2023] [Accepted: 03/29/2023] [Indexed: 04/19/2023]
Abstract
PURPOSE Noninvasive assessment of significant liver fibrosis in rabbits by spectral CT parameters and texture analysis. MATERIALS AND METHODS Thirty-three rabbits were randomly divided into 27 carbon tetrachloride-induced liver fibrosis group and 6 control group. Spectral CT contrast-enhanced scan was performed in batches, and the liver fibrosis was staged according to the histopathological results. The portal venous phase spectral CT parameters [70 keV CT value, normalized iodine concentration (NIC), spectral HU curve slope (λHU)] were measured, and MaZda texture analysis was performed on 70 keV monochrome images. Three dimensionality reduction methods and four statistical methods in B11 module were used to perform discriminant analysis and calculate misclassified rate (MCR), and ten texture features under the lowest combination of MCR were statistically analyzed. Receiver operating characteristic curve (ROC) was used to calculate the diagnostic performance of spectral parameters and texture features for significant liver fibrosis. Finally, the binary logistic regression was used to further screen independent predictors and establish model. RESULTS A total of 23 experimental rabbits and 6 control rabbits were included, of which 16 had significant liver fibrosis. Three spectral CT parameters with significant liver fibrosis were significantly lower than those of non-significant liver fibrosis (p < 0.05), and the AUC ranged from 0.846 to 0.913. The combination analysis of mutual information (MI) and nonlinear discriminant analysis (NDA) had the lowest MCR, which with 0%. In the filtered texture features, four were statistically significant and AUC > 0.5, ranges from 0.764 to 0.875. The logistic regression model showed that Perc.90% and NIC could be used as independent predictors, the overall prediction accuracy of the model was 89.7% and the AUC was 0.976. CONCLUSION Spectral CT parameters and texture features have high diagnostic value for predicting significant liver fibrosis in rabbits, and the combination of the two can improve its diagnostic efficiency.
Collapse
Affiliation(s)
- Xiuru Gong
- Shanghai Municipal Hospital of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, 274 Zhijiang Middle Road, Jing 'an District, Shanghai, 200071, China
| | - Yaxin Guo
- Shanghai Municipal Hospital of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, 274 Zhijiang Middle Road, Jing 'an District, Shanghai, 200071, China
| | - Tingting Zhu
- Shanghai Municipal Hospital of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, 274 Zhijiang Middle Road, Jing 'an District, Shanghai, 200071, China
| | - Dongwei Xing
- Shanghai Municipal Hospital of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, 274 Zhijiang Middle Road, Jing 'an District, Shanghai, 200071, China
| | - Qi Shi
- Shanghai Municipal Hospital of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, 274 Zhijiang Middle Road, Jing 'an District, Shanghai, 200071, China.
| | - Minguang Zhang
- Shanghai Municipal Hospital of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, 274 Zhijiang Middle Road, Jing 'an District, Shanghai, 200071, China.
| |
Collapse
|
4
|
Wang Y, Cui F, Ding X, Yao Y, Li G, Gui G, Shen F, Li B. Automated identification of the preclinical stage of coal workers' pneumoconiosis from digital chest radiography using three-stage cascaded deep learning model. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2023.104607] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
|