MRI-based radiomic signature for MYCN amplification prediction of pediatric abdominal neuroblastoma

Abstract

MYCN gene amplification critically drives neuroblastoma aggressiveness and poor outcomes, necessitating precise preoperative identification to guide risk-adapted therapies. Current invasive detection methods present substantial challenges for pediatric patients. To address this unmet need, we developed a noninvasive MRI-based radiomic signature for predicting MYCN amplification status in childhood abdominal neuroblastoma. In this prospective study, 99 patients with pathologically confirmed abdominal neuroblastoma underwent preoperative MRI between April 2019 and September 2021. From T2-weighted images, 1409 radiomic features were extracted per subject. Through two-sample statistical testing and least absolute shrinkage and selection operator (LASSO) regression, we constructed an optimized radiomic signature incorporating six highly discriminative features. The signature achieved exceptional performance (AUC = 0.91) in predicting MYCN amplification, significantly outperforming neuron-specific enolase levels (AUC = 0.68, p-value < 0.001) and all individual radiomic features. When integrated with neuron-specific enolase via multivariate logistic regression, the model achieved comparable performance (AUC = 0.91) to the signature only. Our findings establish the clinical viability of this MRI-based approach for noninvasively stratifying MYCN amplification status, offering significant potential to optimize surgical planning and therapeutic strategies for pediatric neuroblastoma.