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Background <p>Interstitial lung disease (ILD) is a frequently observed pulmonary manifestation in juvenile dermatomyositis (JDM), and may significantly impact patient outcomes. Therefore, early lung involvement identification is essential. Radiomics is a new image analysis technique and might offer valuable information for the diagnosis of interstitial lung disease in juvenile dermatomyositis (JDM-ILD).</p> Methods <p>We retrospectively analyzed clinical data of 56 children with JDM, and all participants gave written informed consent. These children were divided into the JDM group (<i>n</i> = 32) and JDM-ILD group (<i>n</i> = 24) based on chest high-resolution CT (HRCT). The lung intelligence kit (LK) software was used to outline the bilateral lung tissue structure automatically. The radiomics score combining with clinical variables was used to establish a prediction model for JDM-ILD.</p> Results <p>A total of seven radiomics features including the maximum, mean, skewness, and kurtosis features for the First Order Features, the InverseVariance feature for the Gray Level Co-occurrence Matrix (GLCM) Features, the Size Zone NonUniformity Normalized feature for the Gray Level Size Zone Matrix(GLSZM)Features, and the Run Entropy feature for the Gray Level Run Length Matrix (GLRLM) Features were identified. The multivariable logistic regression revealed that anti-MDA5 antibody and radiomics score showed a significant correlation with the development of ILD in children with JDM. The combined prediction model based on radiomics score and anti-MDA5 antibody achieved good performance in predicting JDM-ILD in the training (0.92, 95% CI 0.82-1.00) and validation (0.93, 95% CI 0.83-1.00) groups.</p> Conclusion <p>The nomogram combining radiomics and clinical variables achieved an optimal prediction of ILD in children with JDM. This approach may facilitate earlier detection of pulmonary involvement, support individualized risk stratification, and guide more proactive therapeutic interventions. Future studies should aim to validate this model in larger, prospective, and multicenter cohorts.</p>

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Application of HRCT-based radiomics to predict interstitial lung disease for juvenile dermatomyositis

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年8月2日

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  • Lingke Liu,
  • Minfei Hu,
  • Yun Zhou,
  • Fei Zheng,
  • Xiaohui Ma,
  • Li Yang,
  • Yiping Xu,
  • Liping Teng,
  • Bin Hu,
  • Meiping Lu,
  • Xuefeng Xu

摘要

Background

Interstitial lung disease (ILD) is a frequently observed pulmonary manifestation in juvenile dermatomyositis (JDM), and may significantly impact patient outcomes. Therefore, early lung involvement identification is essential. Radiomics is a new image analysis technique and might offer valuable information for the diagnosis of interstitial lung disease in juvenile dermatomyositis (JDM-ILD).

Methods

We retrospectively analyzed clinical data of 56 children with JDM, and all participants gave written informed consent. These children were divided into the JDM group (n = 32) and JDM-ILD group (n = 24) based on chest high-resolution CT (HRCT). The lung intelligence kit (LK) software was used to outline the bilateral lung tissue structure automatically. The radiomics score combining with clinical variables was used to establish a prediction model for JDM-ILD.

Results

A total of seven radiomics features including the maximum, mean, skewness, and kurtosis features for the First Order Features, the InverseVariance feature for the Gray Level Co-occurrence Matrix (GLCM) Features, the Size Zone NonUniformity Normalized feature for the Gray Level Size Zone Matrix(GLSZM)Features, and the Run Entropy feature for the Gray Level Run Length Matrix (GLRLM) Features were identified. The multivariable logistic regression revealed that anti-MDA5 antibody and radiomics score showed a significant correlation with the development of ILD in children with JDM. The combined prediction model based on radiomics score and anti-MDA5 antibody achieved good performance in predicting JDM-ILD in the training (0.92, 95% CI 0.82-1.00) and validation (0.93, 95% CI 0.83-1.00) groups.

Conclusion

The nomogram combining radiomics and clinical variables achieved an optimal prediction of ILD in children with JDM. This approach may facilitate earlier detection of pulmonary involvement, support individualized risk stratification, and guide more proactive therapeutic interventions. Future studies should aim to validate this model in larger, prospective, and multicenter cohorts.