Development and validation of a CT‐based radiomics signature for identifying high‐risk neuroblastomas under the revised Children's Oncology Group classification system

Abstract

AbstractBackgroundTo develop and validate a radiomics signature based on computed tomography (CT) for identifying high‐risk neuroblastomas.ProcedureThis retrospective study included 339 patients with neuroblastomas, who were classified into high‐risk and non‐high‐risk groups according to the revised Children's Oncology Group classification system. These patients were then randomly divided into a training set (n = 237) and a testing set (n = 102). Pretherapy CT images of the arterial phase were segmented by two radiologists. Pyradiomics package and FeAture Explorer software were used to extract and process radiomics features. Radiomics models based on linear discriminant analysis (LDA), logistic regression (LR), and support vector machine (SVM) were constructed, and the area under the curve (AUC), 95% confidence interval (CI), and accuracy were calculated.ResultsThe optimal LDA, LR, and SVM models had 11, 12, and 14 radiomics features, respectively. The AUC of the LDA model in the training and testing sets were 0.877 (95% CI: 0.833–0.921) and 0.867 (95% CI: 0.797–0.937), with an accuracy of 0.823 and 0.804, respectively. The AUC of the LR model in the training and testing sets were 0.881 (95% CI: 0.839–0.924) and 0.855 (95% CI: 0.781–0.930), with an accuracy of 0.823 and 0.804, respectively. The AUC of the SVM model in the training and testing sets were 0.879 (95% CI: 0.836–0.923) and 0.862 (95% CI: 0.791–0.934), with an accuracy of 0.827 and 0.804, respectively.ConclusionsCT‐based radiomics is able to identify high‐risk neuroblastomas and may provide additional image biomarkers for the identification of high‐risk neuroblastomas.