Letter to the Editor: Magnetic resonance imaging-based deep learning radiomics for preoperative risk stratification in pediatric hepatoblastoma

Abstract

This letter to the editor discusses a recent multi-institutional study that developed a noninvasive deep learning-based radiomics score derived from preoperative magnetic resonance imaging (MRI) to predict event-free survival in pediatric hepatoblastoma. The original study by Yang and Li published in World Journal of Radiology, leveraged convolutional neural networks to extract high-dimensional features from T1 and T2 sequences, the researchers developed an integrated nomogram that combines these imaging signatures with traditional markers like alpha-fetoprotein and the pretreatment extension of disease stage. This model significantly outperforms standard clinical predictors, offering preliminary evidence for an MRI-based approach to preoperative risk stratification that warrants further large-scale validation.

Keywords: Pediatric hepatoblastoma; Magnetic resonance imaging radiomics; Deep learning; Event-free survival; Preoperative risk stratification; Alpha-fetoprotein; Pretreatment extension of disease stage; Nomogram

Core Tip: Accurate preoperative risk stratification remains challenging in pediatric hepatoblastoma. This study demonstrates that a magnetic resonance imaging-based deep learning radiomics score predicts event-free survival and refines risk stratification beyond conventional clinical factors. Integration with pretreatment extension of disease stage and alpha-fetoprotein improves prognostic accuracy, supporting noninvasive, imaging-driven decision-making for individualized.