Predicting MYCN amplification in paediatric neuroblastoma: development and validation of a 18F-FDG PET/CT-based radiomics signature
Abstract Objectives To develop and validate an 18F-FDG PET/CT-based clinical-radiological-radiomics nomogram and evaluate its value in the diagnosis of MYCN amplification (MNA) in paediatric neuroblastoma (NB) patients. Methods A total of 104 patients with NB were retrospectively included. We constr...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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SpringerOpen
2023-11-01
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| Series: | Insights into Imaging |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s13244-023-01493-8 |
| _version_ | 1797452387726655488 |
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| author | Luo-Dan Qian Shu-Xin Zhang Si-Qi Li Li-Juan Feng Zi-Ang Zhou Jun Liu Ming-Yu Zhang Ji-Gang Yang |
| author_facet | Luo-Dan Qian Shu-Xin Zhang Si-Qi Li Li-Juan Feng Zi-Ang Zhou Jun Liu Ming-Yu Zhang Ji-Gang Yang |
| author_sort | Luo-Dan Qian |
| collection | DOAJ |
| description | Abstract Objectives To develop and validate an 18F-FDG PET/CT-based clinical-radiological-radiomics nomogram and evaluate its value in the diagnosis of MYCN amplification (MNA) in paediatric neuroblastoma (NB) patients. Methods A total of 104 patients with NB were retrospectively included. We constructed a nomogram to predict MNA based on radiomics signatures, clinical and radiological features. The multivariable logistic regression and the least absolute shrinkage and selection operator (LASSO) were used for feature selection. Radiomics models are constructed using decision trees (DT), logistic regression (LR) and support vector machine (SVM) classifiers. A clinical-radiological (C-R) model was developed using clinical and radiological features. A clinical-radiological-radiomics (C-R-R) model was developed using the C-R model of the best radiomics model. The prediction performance was verified by receiver operating characteristic (ROC) curve analysis, calibration curve analysis and decision curve analysis (DCA) in the training and validation cohorts. Results The present study showed that four radiomics signatures were significantly correlated with MNA. The SVM classifier was the best model of radiomics signature. The C-R-R model has the best discriminant ability to predict MNA, with AUCs of 0.860 (95% CI, 0.757–0.963) and 0.824 (95% CI, 0.657–0.992) in the training and validation cohorts, respectively. The calibration curve indicated that the C-R-R model has the goodness of fit and DCA confirms its clinical utility. Conclusion Our research provides a non-invasive C-R-R model, which combines the radiomics signatures and clinical and radiological features based on 18F-FDGPET/CT images, shows excellent diagnostic performance in predicting MNA, and can provide useful biological information with stratified therapy. Critical relevance statement Radiomic signatures of 18F-FDG-based PET/CT can predict MYCN amplification in neuroblastoma. Key points • Radiomic signatures of 18F-FDG-based PET/CT can predict MYCN amplification in neuroblastoma. • SF, LDH, necrosis and TLG are the independent risk factors of MYCN amplification. • Clinical-radiological-radiomics model improved the predictive performance of MYCN amplification. Graphical Abstract |
| first_indexed | 2024-03-09T15:07:41Z |
| format | Article |
| id | doaj.art-3808518c33024ade894d1d68837ce8b6 |
| institution | Directory Open Access Journal |
| issn | 1869-4101 |
| language | English |
| last_indexed | 2024-03-09T15:07:41Z |
| publishDate | 2023-11-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Insights into Imaging |
| spelling | doaj.art-3808518c33024ade894d1d68837ce8b62023-11-26T13:33:11ZengSpringerOpenInsights into Imaging1869-41012023-11-0114111210.1186/s13244-023-01493-8Predicting MYCN amplification in paediatric neuroblastoma: development and validation of a 18F-FDG PET/CT-based radiomics signatureLuo-Dan Qian0Shu-Xin Zhang1Si-Qi Li2Li-Juan Feng3Zi-Ang Zhou4Jun Liu5Ming-Yu Zhang6Ji-Gang Yang7Nuclear Medicine Department, Beijing Friendship Hospital, Capital Medical UniversityNuclear Medicine Department, Beijing Friendship Hospital, Capital Medical UniversityNuclear Medicine Department, Beijing Friendship Hospital, Capital Medical UniversityNuclear Medicine Department, Beijing Friendship Hospital, Capital Medical UniversityNuclear Medicine Department, Beijing Friendship Hospital, Capital Medical UniversityNuclear Medicine Department, Beijing Friendship Hospital, Capital Medical UniversityNuclear Medicine Department, Beijing Friendship Hospital, Capital Medical UniversityNuclear Medicine Department, Beijing Friendship Hospital, Capital Medical UniversityAbstract Objectives To develop and validate an 18F-FDG PET/CT-based clinical-radiological-radiomics nomogram and evaluate its value in the diagnosis of MYCN amplification (MNA) in paediatric neuroblastoma (NB) patients. Methods A total of 104 patients with NB were retrospectively included. We constructed a nomogram to predict MNA based on radiomics signatures, clinical and radiological features. The multivariable logistic regression and the least absolute shrinkage and selection operator (LASSO) were used for feature selection. Radiomics models are constructed using decision trees (DT), logistic regression (LR) and support vector machine (SVM) classifiers. A clinical-radiological (C-R) model was developed using clinical and radiological features. A clinical-radiological-radiomics (C-R-R) model was developed using the C-R model of the best radiomics model. The prediction performance was verified by receiver operating characteristic (ROC) curve analysis, calibration curve analysis and decision curve analysis (DCA) in the training and validation cohorts. Results The present study showed that four radiomics signatures were significantly correlated with MNA. The SVM classifier was the best model of radiomics signature. The C-R-R model has the best discriminant ability to predict MNA, with AUCs of 0.860 (95% CI, 0.757–0.963) and 0.824 (95% CI, 0.657–0.992) in the training and validation cohorts, respectively. The calibration curve indicated that the C-R-R model has the goodness of fit and DCA confirms its clinical utility. Conclusion Our research provides a non-invasive C-R-R model, which combines the radiomics signatures and clinical and radiological features based on 18F-FDGPET/CT images, shows excellent diagnostic performance in predicting MNA, and can provide useful biological information with stratified therapy. Critical relevance statement Radiomic signatures of 18F-FDG-based PET/CT can predict MYCN amplification in neuroblastoma. Key points • Radiomic signatures of 18F-FDG-based PET/CT can predict MYCN amplification in neuroblastoma. • SF, LDH, necrosis and TLG are the independent risk factors of MYCN amplification. • Clinical-radiological-radiomics model improved the predictive performance of MYCN amplification. Graphical Abstracthttps://doi.org/10.1186/s13244-023-01493-8Neuroblastoma18F-FDG PET/CTRadiogenomicsMYCN |
| spellingShingle | Luo-Dan Qian Shu-Xin Zhang Si-Qi Li Li-Juan Feng Zi-Ang Zhou Jun Liu Ming-Yu Zhang Ji-Gang Yang Predicting MYCN amplification in paediatric neuroblastoma: development and validation of a 18F-FDG PET/CT-based radiomics signature Insights into Imaging Neuroblastoma 18F-FDG PET/CT Radiogenomics MYCN |
| title | Predicting MYCN amplification in paediatric neuroblastoma: development and validation of a 18F-FDG PET/CT-based radiomics signature |
| title_full | Predicting MYCN amplification in paediatric neuroblastoma: development and validation of a 18F-FDG PET/CT-based radiomics signature |
| title_fullStr | Predicting MYCN amplification in paediatric neuroblastoma: development and validation of a 18F-FDG PET/CT-based radiomics signature |
| title_full_unstemmed | Predicting MYCN amplification in paediatric neuroblastoma: development and validation of a 18F-FDG PET/CT-based radiomics signature |
| title_short | Predicting MYCN amplification in paediatric neuroblastoma: development and validation of a 18F-FDG PET/CT-based radiomics signature |
| title_sort | predicting mycn amplification in paediatric neuroblastoma development and validation of a 18f fdg pet ct based radiomics signature |
| topic | Neuroblastoma 18F-FDG PET/CT Radiogenomics MYCN |
| url | https://doi.org/10.1186/s13244-023-01493-8 |
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