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

Abstract

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 features model outperformed both the QCT-only model and the clinically-only model. In calibration and clinical decision curve analysis, the final model demonstrated minimal prediction bias (concordance index: 0.887, 95% CI: 0.800-0.974, P<0.001) and provided greater net benefit across most thresholds. The nomogram encompassed the incorporation of the following variables: subtype, gender, forced expiratory volume in one second (FEV₁%), diffusing capacity for carbon monoxide (DL_CO%), oxygenation index (OI), and quantitative ground-glass opacities (GGOs), consolidation, pulmonary vascular, and branches on HRCT.

Conclusions: When utilizing ML techniques, the baseline QCT has the potential to predict rapid progression in patients with IIM-ILD. The prediction performance will be further improved by incorporating clinical data alongside HRCT features.

Keywords: Idiopathic inflammatory myopathy (IIM); rapidly progressive interstitial lung disease (RP-ILD); high-resolution computed tomography (HRCT); machine learning (ML); quantitative computed tomography (QCT).

Figure 1

Flow diagram of eligibility criteria. IIM-ILD, idiopathic inflammatory myopathy related interstitial lung disease; HRCT, high-resolution computed tomography; RP-ILD, rapidly progressive interstitial lung disease.

Figure 2

The progression of IIM-ILD and the model building diagram. HRCT and clinical features of patients after diagnosis of IIM-ILD and before disease progression were used to establish predictive models. This figure was created on www.figdraw.com and has been authorized for use. IIM, idiopathic inflammatory myopathy; IIM-ILD, idiopathic inflammatory myopathy related interstitial lung disease; HRCT, high-resolution computed tomography; RP-ILD, rapidly progressive interstitial lung disease.

Figure 3

Quantitative CT analysis diagram. From left to right, the segmentation of lung lobe, lung interstitial lesions, and pulmonary vasculature were observed. CT, computed tomography.

Figure 4

Performance of a series of models for predicting RP-ILD. (A) ROC of eight machine learning models; (B) ROC of logistic regression; (C-E) calibration curve of logistic regression; (F) clinical decision curve of logistic regression; (G) prognostic nomogram. TPR, true positive rate; AUC, area under the ROC curve; KNN, k-nearest neighbor; SVM, support vector machine; MLP, multilayer perceptron; FPR, false positive rate; HRCT, high-resolution computed tomography; ASS, antisynthetase syndrome; MDA5, melanoma differentiation-associated protein 5; FEV₁%, forced expiratory volume in 1 second as a percentage of the predicted value; DL_CO%, diffusing capacity of the lung for carbon monoxide as a percentage of the predicted value; OI, oxygenation index; RP-ILD, rapidly progressive interstitial lung disease; ROC, receiver operating characteristic.