Radiomic-based machine learning model for predicting the surgical risk in children with abdominal neuroblastoma
Background Preoperative imaging assessment of surgical risk is very important for the prognosis of these children. To develop and validate a radiomics-based machine learning model based on the analysis of radiomics features to predict surgical risk in children with abdominal neuroblastoma (NB).Metho...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
BMJ Publishing Group
2023-07-01
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| Series: | World Journal of Pediatric Surgery |
| Online Access: | https://wjps.bmj.com/content/6/3/e000531.full |
| _version_ | 1797771335382859776 |
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| author | Xiaohui Ma Xuan Jia Jiawei Liang Wenqi Wang Can Lai |
| author_facet | Xiaohui Ma Xuan Jia Jiawei Liang Wenqi Wang Can Lai |
| author_sort | Xiaohui Ma |
| collection | DOAJ |
| description | Background Preoperative imaging assessment of surgical risk is very important for the prognosis of these children. To develop and validate a radiomics-based machine learning model based on the analysis of radiomics features to predict surgical risk in children with abdominal neuroblastoma (NB).Methods A retrospective study was conducted from April 2019 to March 2021 among 74 children with abdominal NB. A total of 1874 radiomic features in MR images were extracted from each patient. Support vector machines (SVMs) were used to establish the model. Eighty percent of the data were used as the training set to optimize the model, and 20% of the data were used to validate its accuracy, sensitivity, specificity and area under the curve (AUC) to verify its effectiveness.Results Among the 74 children with abdominal NB, 55 (65%) had surgical risk and 19 (35%) had no surgical risk. A t test and Lasso identified that 28 radiomic features were associated with surgical risk. After developing an SVM-based model using these features, predictions were made about whether children with abdominal NB had surgical risk. The model achieved an AUC of 0.94 (a sensitivity of 0.83 and a specificity of 0.80) with 0.890 accuracy in the training set and an AUC of 0.81 (a sensitivity of 0.73 and a specificity of 0.82) with 0.838 accuracy in the test set.Conclusions Radiomics and machine learning can be used to predict the surgical risk in children with abdominal NB. The model based on 28 radiomic features established by SVM showed good diagnostic efficiency. |
| first_indexed | 2024-03-12T21:36:03Z |
| format | Article |
| id | doaj.art-f2be005b18114441a0c8544f4fbbeebc |
| institution | Directory Open Access Journal |
| issn | 2516-5410 |
| language | English |
| last_indexed | 2024-03-12T21:36:03Z |
| publishDate | 2023-07-01 |
| publisher | BMJ Publishing Group |
| record_format | Article |
| series | World Journal of Pediatric Surgery |
| spelling | doaj.art-f2be005b18114441a0c8544f4fbbeebc2023-07-27T07:40:07ZengBMJ Publishing GroupWorld Journal of Pediatric Surgery2516-54102023-07-016310.1136/wjps-2022-000531Radiomic-based machine learning model for predicting the surgical risk in children with abdominal neuroblastomaXiaohui Ma0Xuan Jia1Jiawei Liang2Wenqi Wang3Can Lai4Department of Radiology, Children`s Hospital, Zhejiang University School of Medicine, Hangzhou, ChinaDepartment of Radiology, Children`s Hospital, Zhejiang University School of Medicine, Hangzhou, ChinaDepartment of Radiology, Children`s Hospital, Zhejiang University School of Medicine, Hangzhou, ChinaDepartment of Radiology, Children`s Hospital, Zhejiang University School of Medicine, Hangzhou, ChinaDepartment of Radiology, Children`s Hospital, Zhejiang University School of Medicine, Hangzhou, ChinaBackground Preoperative imaging assessment of surgical risk is very important for the prognosis of these children. To develop and validate a radiomics-based machine learning model based on the analysis of radiomics features to predict surgical risk in children with abdominal neuroblastoma (NB).Methods A retrospective study was conducted from April 2019 to March 2021 among 74 children with abdominal NB. A total of 1874 radiomic features in MR images were extracted from each patient. Support vector machines (SVMs) were used to establish the model. Eighty percent of the data were used as the training set to optimize the model, and 20% of the data were used to validate its accuracy, sensitivity, specificity and area under the curve (AUC) to verify its effectiveness.Results Among the 74 children with abdominal NB, 55 (65%) had surgical risk and 19 (35%) had no surgical risk. A t test and Lasso identified that 28 radiomic features were associated with surgical risk. After developing an SVM-based model using these features, predictions were made about whether children with abdominal NB had surgical risk. The model achieved an AUC of 0.94 (a sensitivity of 0.83 and a specificity of 0.80) with 0.890 accuracy in the training set and an AUC of 0.81 (a sensitivity of 0.73 and a specificity of 0.82) with 0.838 accuracy in the test set.Conclusions Radiomics and machine learning can be used to predict the surgical risk in children with abdominal NB. The model based on 28 radiomic features established by SVM showed good diagnostic efficiency.https://wjps.bmj.com/content/6/3/e000531.full |
| spellingShingle | Xiaohui Ma Xuan Jia Jiawei Liang Wenqi Wang Can Lai Radiomic-based machine learning model for predicting the surgical risk in children with abdominal neuroblastoma World Journal of Pediatric Surgery |
| title | Radiomic-based machine learning model for predicting the surgical risk in children with abdominal neuroblastoma |
| title_full | Radiomic-based machine learning model for predicting the surgical risk in children with abdominal neuroblastoma |
| title_fullStr | Radiomic-based machine learning model for predicting the surgical risk in children with abdominal neuroblastoma |
| title_full_unstemmed | Radiomic-based machine learning model for predicting the surgical risk in children with abdominal neuroblastoma |
| title_short | Radiomic-based machine learning model for predicting the surgical risk in children with abdominal neuroblastoma |
| title_sort | radiomic based machine learning model for predicting the surgical risk in children with abdominal neuroblastoma |
| url | https://wjps.bmj.com/content/6/3/e000531.full |
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