Machine learning models for the prediction of preclinical coal workers' pneumoconiosis: integrating CT radiomics and occupational health surveillance records.
Authors
Affiliations (11)
Affiliations (11)
- Environment and Non-Communicable Disease Research Center, School of Public Health, China Medical University, Shenyang, 110122, China.
- Key Laboratory of Environmental Stress and Chronic Disease Control and Prevention, Ministry of Education (China Medical University), Shenyang, Liaoning, People's Republic of China.
- Key Laboratory of Arsenic-Related Biological Effects and Prevention and Treatment in Liaoning Province, No.77 Puhe Road, Shenyang North New Area, Shenyang, 110122, Liaoning, People's Republic of China.
- Occupational Health Surveillance and Management Center, Occupational Disease Prevention and Control Institute, Huaibei Mining Co., Ltd., Huaibei, 235000, China.
- Department of Radiology, Occupational Disease Prevention and Control Institute, Huaibei Mining Co., Ltd., Huaibei, 235000, China.
- School of Public Health, North China University of Science and Technology, Tangshan, 063210, China.
- Beijing Kangyide Integrated Traditional Chinese and Western Medicine Pulmonary Hospital, Beijing, 101400, China.
- Environment and Non-Communicable Disease Research Center, School of Public Health, China Medical University, Shenyang, 110122, China. [email protected].
- Key Laboratory of Environmental Stress and Chronic Disease Control and Prevention, Ministry of Education (China Medical University), Shenyang, Liaoning, People's Republic of China. [email protected].
- Key Laboratory of Arsenic-Related Biological Effects and Prevention and Treatment in Liaoning Province, No.77 Puhe Road, Shenyang North New Area, Shenyang, 110122, Liaoning, People's Republic of China. [email protected].
- Department of Biomedical Engineering, School of Intelligent Medicine, China Medical University, No.77 Puhe Road, Shenyang North New Area, Shenyang, 110122, Liaoning, People's Republic of China. [email protected].
Abstract
This study aims to integrate CT imaging with occupational health surveillance data to construct a multimodal model for preclinical CWP identification and individualized risk evaluation. CT images and occupational health surveillance data were retrospectively collected from 874 coal workers, including 228 Stage I and 4 Stage II pneumoconiosis patients, along with 600 healthy and 42 subcategory 0/1 coal workers. First, the YOLOX was employed for automated 3D lung extraction to extract radiomics features. Second, two feature selection algorithms were applied to select critical features from both CT radiomics and occupational health data. Third, three distinct feature sets were constructed for model training: CT radiomics features, occupational health data, and their multimodal integration. Finally, five machine learning models were implemented to predict the preclinical stage of CWP. The model's performance was evaluated using the receiver operating characteristic curve (ROC), accuracy, sensitivity, and specificity. SHapley Additive exPlanation (SHAP) values were calculated to determine the prediction role of each feature in the model with the highest predictive performance. The YOLOX-based lung extraction demonstrated robust performance, achieving an Average Precision (AP) of 0.98. 8 CT radiomic features and 4 occupational health surveillance data were selected for the multimodal model. The optimal occupational health surveillance feature subset comprised the Length of service. Among 5 machine learning algorithms evaluated, the Decision Tree-based multimodal model showed superior predictive capacity on the test set of 142 samples, with an AUC of 0.94 (95% CI 0.88-0.99), accuracy 0.95, specificity 1.00, and Youden's index 0.83. SHAP analysis indicated that Total Protein Results, original shape Flatness, diagnostics Image original Mean were the most influential contributors. Our study demonstrated that the multimodal model demonstrated strong predictive capability for the preclinical stage of CWP by integrating CT radiomic features with occupational health data.