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

Published version
Peer-reviewed

Repository DOI


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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.

Description

Peer reviewed: True


Publication status: Published

Keywords

classification, machine learning, multi-omics, neuroblastoma, outcome, risk, survival, treatment, Child, Humans, Neuroblastoma, Machine Learning, Multiomics, Patients

Journal Title

Med Sci (Basel)

Conference Name

Journal ISSN

2076-3271
2076-3271

Volume Title

12

Publisher

MDPI AG