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

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.

Study Overview

• Status: Active, not recruiting
• Conditions: Machine Learning; Radiomics; ILD; Dermatomyositis (DM)
• Intervention / Treatment: Other: Observe the medical images; Other: Extracting image feature

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.

• Study Type: Observational
• Enrollment (Actual): 200

Contacts and Locations

Study Locations

• China
  • Shanghai Municipality
    • Shanghai, Shanghai Municipality, China
      • Department of Nuclear Medicine & Institute for medical imaging technology, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine,

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: No

• Sampling Method: Non-Probability Sample

Study Population

Between June 2023 and July 2025, 154 consecutive patients diagnosed with dermatomyositis (DM) who underwent 68Ga-FAPI-04 PET/CT imaging were initially considered. The diagnosis of DM was established based on Bohan and Peter criteria for classic DM [24], or Sontheimer criteria for clinically amyopathic dermatomyositis (CADM)[25]. The diagnosis of interstitial lung disease (ILD) was confirmed by a multidisciplinary team based on a combination of clinical symptoms (cough, dyspnea), physical findings (inspiratory crackles), high-resolution computed tomography (HRCT) evidence of interstitial changes, and pulmonary function tests showing restrictive ventilator defects.

Description

Inclusion Criteria:

1. The diagnosis of dermatomyositis (DM) was made in accordance with the Bohan and Peter criteria
2. The diagnosis of clinically amyopathic dermatomyositis (CADM) was established based on the Sontheimer criteria
3. The diagnosis of interstitial lung disease (ILD) was confirmed in line with the criteria of the American Thoracic Society (ATS)
4. ⁶⁸Ga-FAPI and ¹⁸F-FDG PET/CT scans were performed in the Department of Nuclear Medicine.

Exclusion Criteria: Patients with other connective tissue diseases.

Study Plan

How is the study designed?

Number of groups / cohorts

2

Cohorts and Interventions

Group / Cohort	Intervention / Treatment
ILD; The diagnosis of ILD was confirmed in line with the criteria of the American Thoracic Society (ATS).	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
non-ILD; Patients in the non-ILD group had no evidence of ILD as judged by ATS criteria.	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

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Imaging features of 68Ga-FAPI PET image	conventional PET parameters (SUVmax, SUVmin) and PET textural feature parameters (radiomics)	baseline

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Performance of Machine Learning and Reference Models	ROC curve (Receiver Operating Characteristic curve)、DCA curve (Decision Curve Analysis curve)	baseline

Other Outcome Measures

Outcome Measure	Measure Description	Time Frame
SHAP Analysis of the Optimal Model		baseline
Between-group Differences	DM-ILD and DM non-ILD Group comparison of important parameters in models	baseline
Correlation analysis	Correlation analysis of the various parameters in the model.	baseline

Collaborators and Investigators

• Sponsor: Ruijin Hospital

Study record dates

Study Major Dates

• Study Start (Actual): January 13, 2026
• Primary Completion (Estimated): January 1, 2027
• Study Completion (Estimated): January 1, 2027

Study Registration Dates

• First Submitted: March 27, 2026
• First Submitted That Met QC Criteria: April 11, 2026
• First Posted (Actual): April 15, 2026

Study Record Updates

• Last Update Posted (Actual): April 15, 2026
• Last Update Submitted That Met QC Criteria: April 11, 2026
• Last Verified: January 1, 2026

More Information

Terms related to this study

• Keywords: machine learning; ILD; dermatomyositis; radiomics; positron emission tomography/computed tomography
• Additional Relevant MeSH Terms: Musculoskeletal Diseases; Nervous System Diseases; Muscular Diseases; Neuromuscular Diseases; Connective Tissue Diseases; Skin Diseases; Myositis; Polymyositis; Skin and Connective Tissue Diseases; Dermatomyositis
• Other Study ID Numbers: RuijinH 2026-47

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No