Computed tomography-based radiomic features combined with clinical parameters for predicting post-infectious bronchiolitis obliterans in children with adenovirus pneumonia: a retrospective study
Objectives To develop a model incorporating computed tomography (CT) radiomic features and clinical parameters for predicting bronchiolitis obliterans (BO) with adenovirus pneumonia in children. Methods A total of 165 children with adenovirus pneumonia between October 2013 and February 2020 were enr...
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2025-03-01
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| author | Li Zhang Ling He Guangli Zhang Xiaoyin Tian Haoru Wang Fang Wang Xin Chen Yinglan Zheng Man Li Yang Li Zhengxiu Luo |
| author_facet | Li Zhang Ling He Guangli Zhang Xiaoyin Tian Haoru Wang Fang Wang Xin Chen Yinglan Zheng Man Li Yang Li Zhengxiu Luo |
| author_sort | Li Zhang |
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| description | Objectives To develop a model incorporating computed tomography (CT) radiomic features and clinical parameters for predicting bronchiolitis obliterans (BO) with adenovirus pneumonia in children. Methods A total of 165 children with adenovirus pneumonia between October 2013 and February 2020 were enrolled retrospectively. Among them, BO occurred in 70 patients, and the remaining 95 patients did not have BO. These children were stratified into training and testing groups at a ratio of 7:3. Manual segmentation of lesions in baseline CT images during acute pneumonia was performed to extract radiomic features. Multiple statistical methods were used to determine the best radiomic features. Combined models based on radiomic and clinical features were established via logistic regression (LR), random forest (RF), and support vector machine (SVM) algorithms. Model performance was evaluated via the area under the receiver operating characteristic curve (AUC). Results A total of 2,264 radiomic features were extracted from the lesions, from which 10 optimal radiomic features were ultimately selected. The length of hospitalization, number of pneumonia lobes, and optimal radiomic features were incorporated into the combined models. In the training group, the AUCs of the combined LR, RF and SVM models were 0.946, 0.977, and 0.971, respectively; while in the testing group, they yielded AUCs of 0.890, 0.859, and 0.885, respectively. The predictive performance of these combined models surpassed that of the radiomic and clinical models. Conclusion Combining CT-based radiomic features with clinical parameters can offer an effective noninvasive model to predict BO in children with adenovirus pneumonia. |
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| publishDate | 2025-03-01 |
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| spelling | doaj-art-ac8ee643f4e3459ca5b16667b647103b2025-08-20T03:04:26ZengPeerJ Inc.PeerJ2167-83592025-03-0113e1914510.7717/peerj.19145Computed tomography-based radiomic features combined with clinical parameters for predicting post-infectious bronchiolitis obliterans in children with adenovirus pneumonia: a retrospective studyLi Zhang0Ling He1Guangli Zhang2Xiaoyin Tian3Haoru Wang4Fang Wang5Xin Chen6Yinglan Zheng7Man Li8Yang Li9Zhengxiu Luo10Department of Radiology Children’s Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatric Metabolism and Inflammatory Diseases, Chongqing, ChinaDepartment of Radiology Children’s Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatric Metabolism and Inflammatory Diseases, Chongqing, ChinaDepartment of Respiratory Medicine, Children’s Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatric Metabolism and Inflammatory Diseases, Chongqing, ChinaDepartment of Respiratory Medicine, Children’s Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatric Metabolism and Inflammatory Diseases, Chongqing, ChinaDepartment of Radiology Children’s Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatric Metabolism and Inflammatory Diseases, Chongqing, ChinaDepartment of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, ChinaDepartment of Radiology Children’s Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatric Metabolism and Inflammatory Diseases, Chongqing, ChinaDepartment of Radiology Children’s Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatric Metabolism and Inflammatory Diseases, Chongqing, ChinaDepartment of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, ChinaDepartment of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, ChinaDepartment of Respiratory Medicine, Children’s Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatric Metabolism and Inflammatory Diseases, Chongqing, ChinaObjectives To develop a model incorporating computed tomography (CT) radiomic features and clinical parameters for predicting bronchiolitis obliterans (BO) with adenovirus pneumonia in children. Methods A total of 165 children with adenovirus pneumonia between October 2013 and February 2020 were enrolled retrospectively. Among them, BO occurred in 70 patients, and the remaining 95 patients did not have BO. These children were stratified into training and testing groups at a ratio of 7:3. Manual segmentation of lesions in baseline CT images during acute pneumonia was performed to extract radiomic features. Multiple statistical methods were used to determine the best radiomic features. Combined models based on radiomic and clinical features were established via logistic regression (LR), random forest (RF), and support vector machine (SVM) algorithms. Model performance was evaluated via the area under the receiver operating characteristic curve (AUC). Results A total of 2,264 radiomic features were extracted from the lesions, from which 10 optimal radiomic features were ultimately selected. The length of hospitalization, number of pneumonia lobes, and optimal radiomic features were incorporated into the combined models. In the training group, the AUCs of the combined LR, RF and SVM models were 0.946, 0.977, and 0.971, respectively; while in the testing group, they yielded AUCs of 0.890, 0.859, and 0.885, respectively. The predictive performance of these combined models surpassed that of the radiomic and clinical models. Conclusion Combining CT-based radiomic features with clinical parameters can offer an effective noninvasive model to predict BO in children with adenovirus pneumonia.https://peerj.com/articles/19145.pdfChildrenBronchiolitis obliteransAdenovirus pneumoniaRadiomicsPrediction |
| spellingShingle | Li Zhang Ling He Guangli Zhang Xiaoyin Tian Haoru Wang Fang Wang Xin Chen Yinglan Zheng Man Li Yang Li Zhengxiu Luo Computed tomography-based radiomic features combined with clinical parameters for predicting post-infectious bronchiolitis obliterans in children with adenovirus pneumonia: a retrospective study PeerJ Children Bronchiolitis obliterans Adenovirus pneumonia Radiomics Prediction |
| title | Computed tomography-based radiomic features combined with clinical parameters for predicting post-infectious bronchiolitis obliterans in children with adenovirus pneumonia: a retrospective study |
| title_full | Computed tomography-based radiomic features combined with clinical parameters for predicting post-infectious bronchiolitis obliterans in children with adenovirus pneumonia: a retrospective study |
| title_fullStr | Computed tomography-based radiomic features combined with clinical parameters for predicting post-infectious bronchiolitis obliterans in children with adenovirus pneumonia: a retrospective study |
| title_full_unstemmed | Computed tomography-based radiomic features combined with clinical parameters for predicting post-infectious bronchiolitis obliterans in children with adenovirus pneumonia: a retrospective study |
| title_short | Computed tomography-based radiomic features combined with clinical parameters for predicting post-infectious bronchiolitis obliterans in children with adenovirus pneumonia: a retrospective study |
| title_sort | computed tomography based radiomic features combined with clinical parameters for predicting post infectious bronchiolitis obliterans in children with adenovirus pneumonia a retrospective study |
| topic | Children Bronchiolitis obliterans Adenovirus pneumonia Radiomics Prediction |
| url | https://peerj.com/articles/19145.pdf |
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