An interpretable machine learning model integrating clinical and ct radiomic features for the detection of interstitial lung disease in idiopathic inflammatory myopathies

BACKGROUND: Interstitial lung disease (ILD) is a leading cause of morbidity and mortality in patients with idiopathic inflammatory myopathy (IIM). However, early and accurate identification remains a clinical challenge. We aimed to develop and validate an interpretable machine learning framework to predict ILD presence in IIM patients. METHODS: A retrospective cohort of 115 IIM patients (n = 115, including 70 with ILD) was divided into training (n = 81) and test (n = 34) sets. 1,316 radiomic features were extracted from automated lung segmentations of high-resolution CT (HRCT) scans. To prevent data leakage, feature selection—including correlation filtering, mRMR, and LASSO regression—was performed strictly within the training set. Multiple algorithms were evaluated via five-fold cross-validation, with LightGBM selected as the optimal framework. Independent clinical predictors were identified via multivariable logistic regression and integrated with the Rad-score to construct a combined model, visualized using a nomogram. RESULTS: The combined clinical–radiomic model demonstrated superior discriminative ability, achieving an AUC of 0.877 (95% CI: 0.792–0.962) in the training set and 0.898 (95% CI: 0.791–1.000) in the independent test set, significantly outperforming clinical-only and radiomics-only models. Calibration curves and Decision Curve Analysis (DCA) confirmed the model’s high predictive accuracy and clinical net benefit. SHAP analysis identified key radiomic features (e.g., wavelet.HLL_glcm_MCC) contributing to the model. Serum IgG concentration and presence of cough were identified as independent clinical predictors. CONCLUSION: Our interpretable machine learning nomogram, integrating clinical risk factors and CT radiomics, enables accurate and non-invasive detection of ILD in IIM patients. This framework provides a standardized approach for early screening, supporting timely intervention and improved clinical management for patients with pulmonary involvement.