Machine Learning Approaches for Neuroblastoma Risk Prediction and Stratification.

Abstract

Machine learning (ML) holds great promise in advancing risk prediction and stratification for neuroblastoma, a highly heterogeneous pediatric cancer. By utilizing large-scale biological and clinical data, ML models can detect complex patterns that traditional approaches often overlook, enabling more personalized treatments and better patient outcomes. Various ML techniques, such as support vector machines, random forests, and deep learning, have shown superior performance in predicting survival, relapse, and treatment responses in neuroblastoma patients compared to conventional methods. However, challenges like limited data size, model interpretability, data variability, and difficulties in clinical integration hinder broader adoption. Additionally, ethical concerns related to bias and privacy must be addressed. Future work should focus on improving data quality, enhancing model transparency, and conducting thorough clinical validation. With these advancements, ML has the potential to revolutionize neuroblastoma care by refining early diagnosis, risk assessment, and therapeutic decision-making.