ORIGINAL RESEARCH article
Front. Neurol.
Sec. Pediatric Neurology
Volume 16 - 2025 | doi: 10.3389/fneur.2025.1573398
Development of intelligent tools to predict neuroblastoma risk stratification and overall prognosis based on multiphase enhanced CT and clinical features
Provisionally accepted
- First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
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Background: Neuroblastoma (NB) is a common malignancy in children, and accurate risk stratification and prognostic assessment are essential for personalized treatment. Current tumor assessment methods rely on clinical features and conventional imaging techniques, which have limited predictive accuracy. The aim of this study was to develop a deep learning model based on multiphase enhanced CT images and clinical features to improve the accuracy of risk stratification and prognostic assessment of NB.Methods: Multi-phase enhanced CT images and clinical features from 202 NB patients were collected. Four risk stratification classifiers were developed using the Swin Transformer model and evaluated in training and testing cohorts. Prognostic models were constructed using a combination of multiple machine learning algorithms in conjunction with CT image features and clinical characteristics.Results: Swin-ART based on arterial phase images was the best risk stratification classifier with an AUC of 0.770 (95% CI: 0.613-0.909) and an accuracy of 0.780 in the testing cohort. In the prognostic assessment, the combined model of backward stepwise Cox regression and randomized survival forest (RSF) obtained the highest mean C-index of 0.84. The 1-, 3-, and 5-year AUC values of the optimal prognostic model in the training cohort were 0.93 (95% CI: 0.927-0.942), 0.93 (95% CI: 0.929-0.946), and 0.96 (95% CI: 0.953-0.974), respectively. The corresponding AUC values for the testing cohort were 0.90 (95% CI: 0.857-0.934), 0.87 (95% CI: 0.808-0.928), and 0.91 (95% CI: 0.718-0.977), respectively. Multimodal models outperform single-modality clinical models in both predictive accuracy and stability.This study successfully developed a deep learning model based on multiphase enhanced CT images and clinical features to predict risk stratification and prognosis in NB. The findings provide a new tool for clinical practice and lay the foundation for future precision medicine and personalized treatment.
Keywords: deep learning, swin transformer, Neuroblastoma, multiphase enhanced CT, prognostic
Received: 09 Feb 2025; Accepted: 09 Jun 2025.
Copyright: © 2025 Zhao, Han, Yu, Liu, Zhang and Li. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Juan Li, First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
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