Predicting Neuroblastoma Patient Risk Groups, Outcomes, and Treatment Response Using Machine Learning Methods - A Review

Aug 3, 2026

Neuroblastoma is a pediatric malignancy with high morbidity and mortality, making accurate risk assessment and outcome prediction a core challenge in pediatric oncology. Machine learning methods have been applied to diverse neuroblastoma patient datasets to extract clinical and biological signals and build predictive models. This review catalogs and summarizes studies using machine learning and statistical approaches on multi-omics data, histological sections, and medical images, focusing on stratifying patients by risk groups and predicting outcomes including survival and treatment response. The synthesis emphasizes expression-based predictor models to support future diagnostic and therapeutic directions.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages34

Reading Time1 h 26 min

AccessFree to download

Introduction

Neuroblastoma background and staging

Risk stratification and prognostic differences

Machine learning for neuroblastoma prediction

Data sources: multi-omics, histology, medical images

Predictive targets: outcomes, survival, and treatment response

Review scope and aims

medical sciences Review Predicting Neuroblastoma Patient Risk Groups, Outcomes, and Treatment Response Using Machine Learning Methods: A Review Leila Jahangiri 1,2 Citation: Jahangiri, L. Predicting Neuroblastoma Patient Risk Groups, Outcomes, and Treatment Response Using Machine Learning Methods: A Review. Med. Sci. 2024, 12, 5. [https://](https://)[ ](https://)[doi.org/10.3390/medsci12010005](doi.org/10.3390/medsci12010005) Academic Editor: Tracy Murray-Stewart Received: 4 November 2023 Revised: 28 December 2023 Accepted: 3 January 2024 Published: 6 January 2024 Copyright: © 2024 by the author. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) . 1 School of Science and Technology, Nottingham Trent University, Clifton Site, Nottingham NG11 8NS, UK; [leila.jahangiri@ntu.ac.uk](leila.jahangiri@ntu.ac.uk) 2 Division of Cellular and Molecular Pathology, Addenbrookes Hospital, University of Cambridge, Cambridge CB2 0QQ, UK Abstract: Neuroblastoma, a paediatric malignancy with high rates of cancer-related morbidity and mortality, is of significant interest to the field of paediatric cancers. High-risk NB tumours are usually metastatic and result in survival rates of less than 50% . Machine learning approaches have been applied to various neuroblastoma patient data to retrieve relevant clinical and biological information and develop predictive models. Given this background, this study will catalogue and summarise the literature that has used machine learning and statistical methods to analyse data such as multi-omics, histological sections, and medical images to make clinical predictions. Furthermore, the question will be turned on its head, and the use of machine learning to accurately stratify NB patients by risk groups and to predict outcomes, including survival and treatment response, will be summarised. Overall, this study aims to catalogue and summarise the important work conducted to date on the subject of expression-based predictor models and machine learning in neuroblastoma for risk stratification and patient outcomes including survival, and treatment response which may assist and direct future diagnostic and therapeutic efforts. Keywords: neuroblastoma; machine learning; multi-omics; classification; risk; outcome; survival; treatment 1. Introduction Neuroblastoma (NB) is the second most common malignancy in infants and children, presenting as a tumour of the sympathetic nervous system. Circa 60% of these tumours occur in the abdominal region, and of those, half are located in the medulla of the adrenal glands [1,2] . NB staging is based on the international neuroblastoma risk group staging system (INRGSS) and relies on image-defined risk factors (IDRF), in which locoregional tumours (L1 and L2) display the absence and presence of IDRF, respectively. IDRFs represent surgical risk factors that can be identified in medical images. M displays disseminated disease, and MS encompasses L1 and L2 with metastasis limited to locations such as the skin, liver, and bone marrow (but not cortical bone) in infants younger than 1.5 years (18 months) [3,4] . Previously, the international neuroblastoma staging system (INSS) was utilised and included stages 1 and 2, which encompass locoregional tumours that can be completely or partially resected, respectively, while stage 3, which crosses the midline, is unresectable and unilateral and may or may not involve local lymph nodes. Stage 4 describes distant metastasis of any primary tumour, and 4S represents stages 1 or 2 tumours with limited metastasis to the liver, skin, and bone marrow (but not cortical bone) in children younger than the age of 12 months [3,4] . Risk stratification in

FAQs

What is the purpose of applying machine learning to neuroblastoma datasets?

Machine learning is used to extract relevant clinical and biological information from patient data and to develop predictive models for patient subgrouping, risk, and outcomes, including survival and treatment response.

Which neuroblastoma risk group systems and features are discussed in the review background?

The review describes the international neuroblastoma risk group staging system (INRGSS) based on image-defined risk factors (IDRF) and also contrasts it with the international neuroblastoma staging system (INSS), including stages 1–4 and 4S.

What kinds of patient data are used to build machine learning prediction models?

Studies summarized in the review analyze structured and unstructured data, including multi-omics, histological sections, and medical images, to support clinical prediction tasks.

Aug 3, 2026

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