Published in:
01-05-2021 | Computed Tomography | Paediatric
Radiogenomics of neuroblastoma in pediatric patients: CT-based radiomics signature in predicting MYCN amplification
Published in: European Radiology | Issue 5/2021
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Objectives
To construct a CT-based radiomics signature and assess its performance in predicting MYCN amplification (MNA) in pediatric patients with neuroblastoma.
Methods
Seventy-eight pediatric patients with neuroblastoma were recruited (55 in training cohort and 23 in test cohort). Radiomics features were extracted automatically from the region of interest (ROI) manually delineated on the three-phase computed tomography (CT) images. Selected radiomics features were retained to construct radiomics signature and a radiomics score (rad-score) was calculated by using the radiomics signature–based formula. A clinical model was established with clinical factors, including clinicopathological data, and CT image features. A combined nomogram was developed with the incorporation of a radiomics signature and clinical factors. The predictive performance was assessed by receiver operating characteristics curve (ROC) analysis and decision curve analysis (DCA).
Results
The radiomics signature was constructed using 7 selected radiomics features. The clinical radiomics nomogram, which was based on the radiomics signature and two clinical factors, showed superior predictive performance compared with the clinical model alone (area under the curve (AUC) in the training cohort: 0.95 vs. 0.82, the test cohort: 0.91 vs. 0.70). The clinical utility of clinical radiomics nomogram was confirmed by DCA.
Conclusions
This proposed CT-based radiomics signature was able to predict MNA. Combining the radiomics signature with clinical factors outperformed using clinical model alone for MNA prediction.
Key Points
• A CT-based radiomics signature has the ability to predict MYCN amplification (MNA) in neuroblastoma.
• Both pre- and post-contrast CT images are valuable in predicting MNA.
• Associating the radiomics signature with clinical factors improved the predictive performance of MNA, compared with clinical model alone.