NCT07531446

Construction of a Clinico-Imaging Collaborative Diagnostic Model for Dermatomyositis Combined With Interstitial Lung Disease Based on PET/CT Imaging Features and Clinical Parameters

• Status: Active Not Recruiting
• Phase: —
• Study type: Observational
• Enrollment: 200 (actual)
• Sponsor: Ruijin Hospital · Academic / Other
• Sex: All
• Age: 18 Years
• Healthy volunteers: Not accepted

Summary

The investigators investigated the associations between the imaging parameters of ⁶⁸Ga-FAPI and ¹⁸F-FDG dual-tracer PET/CT and concomitant interstitial lung disease (ILD) in patients with dermatomyositis (DM), developed a novel diagnostic model to predict DM complicated with ILD, and conducted external validation of this model. Meanwhile, the investigators compared the predictive performance of the imaging-only model with that of the classic clinical model and the clinical-radiological collaborative model.

Detailed description

For the features included in the final optimal model, between-group comparisons of continuous variables (interstitial lung disease group vs. non-interstitial lung disease group) were performed using the Wilcoxon rank-sum test. For categorical variables, the Chi-square test or Fisher's exact test was adopted as appropriate.In the comparison of model efficacy, the DeLong test was used to assess the statistical differences in AUC values between each machine learning classifier and the reference model.All statistical analyses were conducted using R software (version 4.4.1). The corresponding R packages applied included pROC for ROC analysis, caret for model training, and SHAP for the interpretability analysis of the XGBoost model. A two-tailed p-value \< 0.05 was defined as the threshold of statistical significance for all analyses.

Conditions

• Dermatomyositis (DM)
• ILD
• Machine Learning
• Radiomics

Interventions

Type	Name	Description
OTHER	Observe the medical images	Observe the medical images via work station or local image analysing software
OTHER	Extracting image feature	Extracting image feature via radiomics or machine learning methods

Timeline

• Start date: 2026-01-13
• Primary completion: 2027-01-01
• Completion: 2027-01-01
• First posted: 2026-04-15
• Last updated: 2026-04-15

Locations

1 site across 1 country: China