Prediction of Pulmonary Fibrosis Progression in Coalworker Pneumoconiosis with a Machine Learning Model Combining CT Radiomics and Clinical Features

Yongsheng Liu^1*Xiaobing Li¹Qian Li¹Xiaobing Li²Wei Wang³Xuemei Li⁴Tingqiang Zhang¹Li Wang¹Li Zhang¹Wutao Xie¹

• ¹Chongqing Medical and Pharmaceutical College, Chongqing, China
• ²Hebei Engineering University, Handan, Hebei Province, China
• ³The First Affiliated Hospital of Chongqing Medical and Pharmaceutical College, Chongqing, China
• ⁴Chongqing Medical University, Chongqing, China

This study aims to develop a machine learning model that integrates CT radiomics with clinical features to predict the progression of pulmonary interstitial fibrosis in patients with coalworker pneumoconiosis. [Methods]: Clinical and imaging data from 297 patients diagnosed with coalworker pneumoconiosis at The First Affiliated Hospital of Chongqing Medical and Pharmaceutical College between December 2021 and December 2023 were analyzed. Of these patients, 170 developed pulmonary interstitial fibrosis over a three-year follow-up and were classified as the progression group, while 127 patients showed stable conditions and were classified as the stable group. The patients were divided into a training cohort (n=207) and a test cohort (n=90). Radiomic features were extracted from CT images of lung fibrosis lesions in the training cohort. These features were reduced in dimensionality to construct morphological biomarkers. Machine learning methods were then used to develop three models: a clinical model, a radiomics model, and a multimodal joint model. The performance of these models was evaluated in the test cohort using receiver operating characteristic (ROC) curves and decision curve analysis (DCA). [Results]: In the training cohort, the area under the curve (AUC) for the clinical, radiomics, and joint models were 0.835, 0.879, and 0.945, respectively. In the test cohort, the AUC values for these models were 0.732, 0.750, and 0.845, respectively. The joint model demonstrated the highest predictive performance and clinical benefit in both the training and test cohorts. [Conclusion]: The multimodal model, combining CT radiomics and clinical features, offers an effective and accurate tool for predicting the progression of pulmonary fibrosis in coalworker pneumoconiosis.