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.