ArticleArthritis research & therapy2026
An interpretable machine learning model integrating clinical and CT radiomic features for the detection of interstitial lung disease in idiopathic inflammatory myopathies.
Article in Arthritis research & therapy, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors.
Funding
Abstract
backgroundInterstitial 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.
methodsA 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.
resultsThe 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.
conclusionOur 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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.