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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.
摘要：
神经母细胞瘤,与癌症相关的发病率和死亡率很高的儿科恶性肿瘤，对儿科癌症领域具有重要意义。高危NB肿瘤通常是转移性的，存活率低于50%。机器学习方法已被应用于各种神经母细胞瘤患者数据以检索相关的临床和生物学信息并开发预测模型。鉴于这一背景,本研究将对使用机器学习和统计方法分析数据的文献进行分类和总结，组织学切片,和医学图像来进行临床预测。此外，这个问题将被颠倒过来，以及使用机器学习按风险组准确地对NB患者进行分层并预测结果，包括生存和治疗反应，将被总结。总的来说,这项研究旨在对迄今为止在神经母细胞瘤中基于表达的预测模型和机器学习进行的重要工作进行分类和总结，以进行风险分层和患者预后，包括生存，和治疗反应，这可能有助于和指导未来的诊断和治疗工作。
