Machine learning-based radiomics model for accurately predicting subtypes of neuroblastoma in children: A retrospective analysis

Objectives： This research seeks to create and validate radiomic models designed to enhance the diagnostic precision of neuroblastoma (NB), focusing specifically on distinguishing high-risk subtypes within its pathological classification. Methods: This retrospective study included 96 cases of NB, confirmed by histopathological evaluation. The cases were categorized according to the WHO classification into an aggressive group (n=55; neuroblastoma, Schwannian stroma-poor and ganglioneuroblastoma, nodular) and an indolent group (n=41; ganglioneuroma and ganglioneuroblastoma, intermixed). Radiomics features were extracted from CT images prior to biopsy or surgical resection. A radiomics model was constructed to predict NB classification using a random forest classifier. ROC curves were used to validate the capability of the models in the training and testing cohorts. Results: The final radiomics model incorporated 9 discriminative features. The model demonstrated strong diagnostic performance with an AUC of 0.874 in testing set, achieving a sensitivity of 77.5% and specificity of 88.6% in pathological classification. Decision curve analysis confirmed clinical utility across probability thresholds of 0.01-0.98 (training) and 0.01-0.81 (testing), indicating broad applicability for risk stratification. Conclusions： The developed radiomics model significantly improves diagnostic accuracy for neuroblastoma pathological classification, addressing a critical gap in current clinical practice. Unlike prior studies limited to binary NB diagnosis, this work provides granular discrimination between aggressive and indolent NB subtypes, enabling more precise risk stratification. While demonstrating robust performance, future multi-center validation is warranted to enhance generalizability and mitigate potential selection bias. This approach establishes a foundation for image-guided precision medicine in pediatric oncology.