Application of HRCT-based radiomics to predict interstitial lung disease for juvenile dermatomyositis
Abstract 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...
Saved in:
| Main Authors: | , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
BMC
2025-08-01
|
| Series: | BMC Pediatrics |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12887-025-05968-z |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849761269667594240 |
|---|---|
| author | Lingke Liu Minfei Hu Yun Zhou Fei Zheng Xiaohui Ma Li Yang Yiping Xu Liping Teng Bin Hu Meiping Lu Xuefeng Xu |
| author_facet | Lingke Liu Minfei Hu Yun Zhou Fei Zheng Xiaohui Ma Li Yang Yiping Xu Liping Teng Bin Hu Meiping Lu Xuefeng Xu |
| author_sort | Lingke Liu |
| collection | DOAJ |
| description | Abstract 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. |
| format | Article |
| id | doaj-art-abdca470ade84f7f9ffd61f9e49d1982 |
| institution | DOAJ |
| issn | 1471-2431 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Pediatrics |
| spelling | doaj-art-abdca470ade84f7f9ffd61f9e49d19822025-08-20T03:06:05ZengBMCBMC Pediatrics1471-24312025-08-0125111110.1186/s12887-025-05968-zApplication of HRCT-based radiomics to predict interstitial lung disease for juvenile dermatomyositisLingke Liu0Minfei Hu1Yun Zhou2Fei Zheng3Xiaohui Ma4Li Yang5Yiping Xu6Liping Teng7Bin Hu8Meiping Lu9Xuefeng Xu10Department of Rheumatology Immunology & Allergy, The Children’s Hospital, National Clinical Research Center for Child Health, Zhejiang University School of MedicineDepartment of Rheumatology Immunology & Allergy, The Children’s Hospital, National Clinical Research Center for Child Health, Zhejiang University School of MedicineDepartment of Rheumatology Immunology & Allergy, The Children’s Hospital, National Clinical Research Center for Child Health, Zhejiang University School of MedicineDepartment of Rheumatology Immunology & Allergy, The Children’s Hospital, National Clinical Research Center for Child Health, Zhejiang University School of MedicineMedical Imaging, The Children’s Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child HealthMedical Imaging, The Children’s Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child HealthDepartment of Rheumatology Immunology & Allergy, The Children’s Hospital, National Clinical Research Center for Child Health, Zhejiang University School of MedicineDepartment of Rheumatology Immunology & Allergy, The Children’s Hospital, National Clinical Research Center for Child Health, Zhejiang University School of MedicineDepartment of Rheumatology Immunology & Allergy, The Children’s Hospital, National Clinical Research Center for Child Health, Zhejiang University School of MedicineDepartment of Rheumatology Immunology & Allergy, The Children’s Hospital, National Clinical Research Center for Child Health, Zhejiang University School of MedicineDepartment of Rheumatology Immunology & Allergy, The Children’s Hospital, National Clinical Research Center for Child Health, Zhejiang University School of MedicineAbstract 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.https://doi.org/10.1186/s12887-025-05968-zHigh-resolution CTInterstitial lung diseaseJuvenile dermatomyositisNomogramRadiomics |
| spellingShingle | Lingke Liu Minfei Hu Yun Zhou Fei Zheng Xiaohui Ma Li Yang Yiping Xu Liping Teng Bin Hu Meiping Lu Xuefeng Xu Application of HRCT-based radiomics to predict interstitial lung disease for juvenile dermatomyositis BMC Pediatrics High-resolution CT Interstitial lung disease Juvenile dermatomyositis Nomogram Radiomics |
| title | Application of HRCT-based radiomics to predict interstitial lung disease for juvenile dermatomyositis |
| title_full | Application of HRCT-based radiomics to predict interstitial lung disease for juvenile dermatomyositis |
| title_fullStr | Application of HRCT-based radiomics to predict interstitial lung disease for juvenile dermatomyositis |
| title_full_unstemmed | Application of HRCT-based radiomics to predict interstitial lung disease for juvenile dermatomyositis |
| title_short | Application of HRCT-based radiomics to predict interstitial lung disease for juvenile dermatomyositis |
| title_sort | application of hrct based radiomics to predict interstitial lung disease for juvenile dermatomyositis |
| topic | High-resolution CT Interstitial lung disease Juvenile dermatomyositis Nomogram Radiomics |
| url | https://doi.org/10.1186/s12887-025-05968-z |
| work_keys_str_mv | AT lingkeliu applicationofhrctbasedradiomicstopredictinterstitiallungdiseaseforjuveniledermatomyositis AT minfeihu applicationofhrctbasedradiomicstopredictinterstitiallungdiseaseforjuveniledermatomyositis AT yunzhou applicationofhrctbasedradiomicstopredictinterstitiallungdiseaseforjuveniledermatomyositis AT feizheng applicationofhrctbasedradiomicstopredictinterstitiallungdiseaseforjuveniledermatomyositis AT xiaohuima applicationofhrctbasedradiomicstopredictinterstitiallungdiseaseforjuveniledermatomyositis AT liyang applicationofhrctbasedradiomicstopredictinterstitiallungdiseaseforjuveniledermatomyositis AT yipingxu applicationofhrctbasedradiomicstopredictinterstitiallungdiseaseforjuveniledermatomyositis AT lipingteng applicationofhrctbasedradiomicstopredictinterstitiallungdiseaseforjuveniledermatomyositis AT binhu applicationofhrctbasedradiomicstopredictinterstitiallungdiseaseforjuveniledermatomyositis AT meipinglu applicationofhrctbasedradiomicstopredictinterstitiallungdiseaseforjuveniledermatomyositis AT xuefengxu applicationofhrctbasedradiomicstopredictinterstitiallungdiseaseforjuveniledermatomyositis |