Predicting MYCN amplification in paediatric neuroblastoma: development and validation of a 18F-FDG PET/CT-based radiomics signature

• Published in: Insights into imaging 2023-11, Vol.14 (1), p.205-205, Article 205
• Main Authors: Qian, Luo-Dan, Zhang, Shu-Xin, Li, Si-Qi, Feng, Li-Juan, Zhou, Zi-Ang, Liu, Jun, Zhang, Ming-Yu, Yang, Ji-Gang
• Format: Article
• Language: English
• Subjects: 18F-FDG PET/CT; Amplification; Calibration; Classifiers; Computed tomography; Decision analysis; Decision trees; Diagnostic Radiology; Fluorine isotopes; Goodness of fit; Imaging; Internal Medicine; Interventional Radiology; Medicine; Medicine & Public Health; MYCN; Necrosis; Neuroblastoma; Neuroradiology; Nomograms; Original Article; Pediatrics; Performance prediction; Radiogenomics; Radiology; Radiomics; Regression analysis; Signatures; Support vector machines; Ultrasound
• ISSN: 1869-4101; 1869-4101
• DOI: 10.1186/s13244-023-01493-8

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