Development of a machine learning model in prediction of the rapid progression of interstitial lung disease in patients with idiopathic inflammatory myopathy

作者：Yang Qiang, Hongyi Wang, Yifei Ni, Jianping Wang, Anqi Liu, Haoyu Yang, Linfeng Xi, Yanhong Ren, Bing Xie, Shiyao Wang, Min Liu, Chen Wang, Huaping Dai · 发表于：Quantitative Imaging in Medicine and Surgery · 年份：2024 · DOI：10.21037/qims-24-595 · 被引用次数：6 · 研究领域：Interstitial Lung Diseases and Idiopathic Pulmonary Fibrosis、Inflammatory Myopathies and Dermatomyositis、Sarcoidosis and Beryllium Toxicity Research

Background: Rapidly progressive interstitial lung disease (RP-ILD) significantly impacts the prognosis of patients with idiopathic inflammatory myopathies (IIM). High-resolution computed tomography (HRCT) is a crucial noninvasive technique for evaluating interstitial lung disease (ILD). Utilizing quantitative computed tomography (QCT) enables accurate quantification of disease severity and evaluation of prognosis, thereby serving as a crucial computer-aided diagnostic method. This study aimed to establish and validate a machine learning (ML) model to predict RP-ILD in patients with idiopathic inflammatory myopathy-related interstitial lung disease (IIM-ILD) based on QCT and clinical features. Methods: A total of 514 patients (367 females, median age 54 years) with IIM-ILD in the China-Japan Friendship Hospital were retrospectively included, out of which 249 cases (165 females, median age 55 years) were identified as having RP-ILD. To extract the quantitative features on HRCT, deep learning (DL) methods were employed, along with demographic factors, pulmonary function test results, and blood gas analysis results; these factors were integrated into a final prediction model. Results: Logistic regression was chosen as the final model due to its superior area under the curve (AUC) and explainability compared to the other seven ML models. The validation dataset yielded an AUC of 0.882 [95% confidence interval (CI): 0.797–0.967], indicating that the combined QCT and clinical feature...