OBJECTIVE  To use radiomics technology based on contrast enhanced computed tomography(CECT) images. It seeks to identify key indicators that can effectively predict neuroblastoma(NB) risk stratification. Then, it will build corresponding prediction models to help with precise drug use for NB.

METHODS  A retrospective method was adopted to collect patients admitted to Children’s Hospital Zhejiang University School of Medicine from August 2018 to December 2023. The clinical information, imaging data, laboratory test results, etc. of the patients were collected, and the radiomics features were extracted from the CECT images using the Python-package Pyradiomics. The image defined risk factor(IDRF) was statistically analyzed by radiologists using CECT images. After screening the radiomics features, IDRF features and clinical information features, the risk classification prediction models were established respectively by using logistic regression, support vector machine and random forest algorithm. Model performance was evaluated using area under the curve(AUC), decision curve analysis(DCA) curves, and calibration curves, and nomograms were developed.

RESULT  A total of 111 NB patients were finally included in this study, among whom 71 were in the high-risk group and 40 were in the medium-low risk group. By analyzing the radiomics features, IDRF features and clinical information features, finally 7 radiomics features, 4 IDRF features and 4 clinical features were screened out to construct the radiomics model, clinical prediction model and IDRF model respectively. The AUC values of the radiomics model ranged from 0.82 to 0.85, those of the clinical prediction model ranged from 0.87 to 0.96, and those of the IDRF model ranged from 0.80 to 0.81. The analysis of DCA curves and correction curves shows that the combined application of the radiomics model and the IDRF model has significantly improved in terms of stability and clinical benefits.

CONCLUSION  The radiomics model developed in this study shows good predictive ability for NB risk stratification, offering a key reference for personalized NB drug therapy.