ArticleFrontiers in neurology2025
Development of intelligent tools to predict neuroblastoma risk stratification and overall prognosis based on multiphase enhanced CT and clinical features.
Article in Frontiers in neurology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
- Single-Cell RNA Sequencing Reveals Lactylation Modifications in Neuroblastoma and the Construction of a Prognostic Model.Molecules (Basel, Switzerland) · 2026Article
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6 authors.
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Abstract
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. Conclusion: 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.
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