Risk stratification in neuroblastoma patients through machine learning in the multicenter PRIMAGE cohort.

ABSTRACT:

Introduction

Neuroblastoma, the most prevalent solid cancer in children, presents significant biological and clinical heterogeneity. This inherent heterogeneity underscores the need for more precise prognostic markers at the time of diagnosis to enhance patient stratification, allowing for more personalized treatment strategies. In response, this investigation developed a machine learning model using clinical, molecular, and magnetic resonance (MR) radiomics features at diagnosis to predict patient's overall survival (OS) and improve their risk stratification.

Methods

PRIMAGE database, including 513 patients (discovery cohort), was used for model training, validation, and testing. Additional 22 patients from different hospitals served as an external independent cohort. Prima

SUBMITTER: Lozano-Montoya J 

PROVIDER: S-EPMC11886962 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature