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

Jose Lozano-Montoya1, Ana Jimenez-Pastor1, Almudena Fuster-Matanzo1

  • 1Research & Frontiers in AI Department, Quantitative Imaging Biomarkers in Medicine, Quibim SL, Valencia, Spain.

Frontiers in Oncology
|March 10, 2025
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Summary

Machine learning models using radiomics features can improve risk stratification for neuroblastoma, a common childhood cancer. This approach enhances patient classification for more personalized treatment strategies.

Keywords:
PRIMAGEmachine learningneuroblastomaoverall survivalpediatricrisk stratification

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Area of Science:

  • Pediatric Oncology
  • Medical Imaging
  • Machine Learning

Background:

  • Neuroblastoma is the most common childhood solid cancer, characterized by significant heterogeneity.
  • Existing prognostic markers necessitate improvement for precise patient stratification and personalized treatment.

Purpose of the Study:

  • To develop a machine learning model for predicting neuroblastoma patient overall survival (OS).
  • To enhance risk stratification using clinical, molecular, and magnetic resonance (MR) radiomics features at diagnosis.

Main Methods:

  • Utilized the PRIMAGE database (513 patients) for training and validation, and an independent cohort (22 patients) for external testing.
  • Extracted 107 radiomics features from T2-weighted MR images, harmonized them using ComBat, and employed nested cross-validation.
  • Developed a prediction model using a random survival forest approach.

Main Results:

  • The model achieved high performance in both discovery (C-index: 78.8 ± 4.9, AUC: 77.7 ± 6.1) and independent cohorts (C-index: 93.4, AUC: 95.4).
  • Key predictive factors included lesion heterogeneity, size, and molecular variables.
  • The model effectively stratified patients into low-, intermediate-, and high-risk groups, outperforming the standard-of-care.

Conclusions:

  • Radiomics features significantly improve the risk stratification systems currently used for neuroblastoma patients.
  • This machine learning approach offers a promising tool for personalized treatment strategies in pediatric neuroblastoma.