Risk stratification in neuroblastoma patients through machine learning in the multicenter PRIMAGE cohort
Neuroblastoma, the most common solid cancer in children, is highly heterogeneous and requires more advanced prognostic markers to enable personalized treatment.
In our latest research, published in Frontiers in Oncology, we developed a machine learning model that integrates clinical, molecular, and radiomic MRI features to predict overall survival (OS) and improve risk classification in neuroblastoma patients. Our model outperformed standard risk classification, providing more accurate stratification into low-, intermediate-, and high-risk groups.
The findings suggest that incorporating radiomics features can significantly enhance current risk stratification systems, potentially leading to more precise and personalized treatment strategies for this high-risk pediatric cancer.
Lozano-Montoya J, Jimenez-Pastor A, Fuster-Matanzo A, Weiss GJ., Cerda-Alberich L, Veiga-Canuto D, Martínez-de-Las-Heras B, Cañete-Nieto A, Taschner-Mandl S, Hero B, Simon T, Ladenstein R, Marti-Bonmati L, Alberich-Bayarri A (2025) Risk stratification in neuroblastoma patients through machine learning in the multicenter PRIMAGE cohort. Front. Oncol. 15:1528836.