Rib suppression-based radiomics for diagnosis of neonatal respiratory distress syndrome in chest X-rays
Abstract This study aims to refine a radiomics-based diagnostic approach for detecting neonatal respiratory distress syndrome (NRDS) and examines the influence of rib suppression on the diagnostic precision of radiomics models using neonatal chest X-ray (CXR) images. A total of 138 CXR images were c...
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Nature Portfolio
2025-02-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-88982-6 |
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| author | Yudong Wang Zongjin Qu Zhengjun Dai Yuhong Li Yanyan Liu Wei Wang Lianxiang Xiao Yi Zhang |
| author_facet | Yudong Wang Zongjin Qu Zhengjun Dai Yuhong Li Yanyan Liu Wei Wang Lianxiang Xiao Yi Zhang |
| author_sort | Yudong Wang |
| collection | DOAJ |
| description | Abstract This study aims to refine a radiomics-based diagnostic approach for detecting neonatal respiratory distress syndrome (NRDS) and examines the influence of rib suppression on the diagnostic precision of radiomics models using neonatal chest X-ray (CXR) images. A total of 138 CXR images were collected in this study. The data was partitioned into training and validation subsets based on chronological order. We applied rib suppression to the CXR images and extracted and analyzed radiomic features from lung regions both before and after rib suppression. This approach was designed to identify NRDS, develop radiomics models, and assess the impact of rib suppression on model performance. To establish these radiomics models, six machine learning models were utilized in the study. The performance was evaluated using the area under the receiver operating characteristic curve (AUC). On the validation set, the models demonstrated significant improvements after rib suppression. Specifically, the Gradient Boosting Machine (GBM) achieved an AUC of 0.781 post-suppression compared to 0.556 pre-suppression. Notably, Linear Discriminant Analysis (LDA) and Logistic Regression (LR) performed particularly well when combining features from both scenarios, achieving AUCs of 0.762 and 0.756. The results indicate the feasibility of developing radiomics models for diagnosing NRDS and highlight the enhancement in model performance due to rib suppression. This study provides a promising new method for the imaging diagnosis and prognosis evaluation of neonatal respiratory distress syndrome, showcasing the potential of radiomics in pediatric imaging. |
| format | Article |
| id | doaj-art-1cc4b7ef9ffd4aa1a327d9972034ad9e |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Nature Portfolio |
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| series | Scientific Reports |
| spelling | doaj-art-1cc4b7ef9ffd4aa1a327d9972034ad9e2025-02-09T12:35:36ZengNature PortfolioScientific Reports2045-23222025-02-0115111110.1038/s41598-025-88982-6Rib suppression-based radiomics for diagnosis of neonatal respiratory distress syndrome in chest X-raysYudong Wang0Zongjin Qu1Zhengjun Dai2Yuhong Li3Yanyan Liu4Wei Wang5Lianxiang Xiao6Yi Zhang7School of Physics and Electronic Engineering, Linyi UniversitySchool of Medicine, Linyi UniversityScientific Research Department, Huiying Medical Technology Co., LtdDepartment of Neonatology, Shandong Provincial Maternal and Child Health Care Hospital affiliated to Qingdao UniversityDepartment of Radiology, Shandong Provincial Maternal and Child Health Care Hospital affiliated to Qingdao UniversityOutpatient Department, Shandong Provincial Maternal and Child Health Care Hospital affiliated to Qingdao UniversityDepartment of Radiology, Shandong Provincial Maternal and Child Health Care Hospital affiliated to Qingdao UniversityInformation Center, Shandong Provincial Maternal and Child Health Care Hospital affiliated to Qingdao UniversityAbstract This study aims to refine a radiomics-based diagnostic approach for detecting neonatal respiratory distress syndrome (NRDS) and examines the influence of rib suppression on the diagnostic precision of radiomics models using neonatal chest X-ray (CXR) images. A total of 138 CXR images were collected in this study. The data was partitioned into training and validation subsets based on chronological order. We applied rib suppression to the CXR images and extracted and analyzed radiomic features from lung regions both before and after rib suppression. This approach was designed to identify NRDS, develop radiomics models, and assess the impact of rib suppression on model performance. To establish these radiomics models, six machine learning models were utilized in the study. The performance was evaluated using the area under the receiver operating characteristic curve (AUC). On the validation set, the models demonstrated significant improvements after rib suppression. Specifically, the Gradient Boosting Machine (GBM) achieved an AUC of 0.781 post-suppression compared to 0.556 pre-suppression. Notably, Linear Discriminant Analysis (LDA) and Logistic Regression (LR) performed particularly well when combining features from both scenarios, achieving AUCs of 0.762 and 0.756. The results indicate the feasibility of developing radiomics models for diagnosing NRDS and highlight the enhancement in model performance due to rib suppression. This study provides a promising new method for the imaging diagnosis and prognosis evaluation of neonatal respiratory distress syndrome, showcasing the potential of radiomics in pediatric imaging.https://doi.org/10.1038/s41598-025-88982-6Neonatal respiratory distress syndromeRadiomicsBone suppressionChest X-rayDeep learning |
| spellingShingle | Yudong Wang Zongjin Qu Zhengjun Dai Yuhong Li Yanyan Liu Wei Wang Lianxiang Xiao Yi Zhang Rib suppression-based radiomics for diagnosis of neonatal respiratory distress syndrome in chest X-rays Scientific Reports Neonatal respiratory distress syndrome Radiomics Bone suppression Chest X-ray Deep learning |
| title | Rib suppression-based radiomics for diagnosis of neonatal respiratory distress syndrome in chest X-rays |
| title_full | Rib suppression-based radiomics for diagnosis of neonatal respiratory distress syndrome in chest X-rays |
| title_fullStr | Rib suppression-based radiomics for diagnosis of neonatal respiratory distress syndrome in chest X-rays |
| title_full_unstemmed | Rib suppression-based radiomics for diagnosis of neonatal respiratory distress syndrome in chest X-rays |
| title_short | Rib suppression-based radiomics for diagnosis of neonatal respiratory distress syndrome in chest X-rays |
| title_sort | rib suppression based radiomics for diagnosis of neonatal respiratory distress syndrome in chest x rays |
| topic | Neonatal respiratory distress syndrome Radiomics Bone suppression Chest X-ray Deep learning |
| url | https://doi.org/10.1038/s41598-025-88982-6 |
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