脑肿瘤是儿童中最常见的实体瘤，尽管与成人相比发病率较低。然而，它们固有的异质性、儿科患者的伦理考虑以及长期随访的困难使得收集大量同质数据集进行分析具有挑战性。本研究的重点是使用成人 BraTS 2020 数据集开发用于脑肿瘤表征的卷积神经网络 (CNN)。我们建议在未来的研究中，通过利用迁移学习（TL），将知识从在广泛的成人脑肿瘤数据集上预先训练的模型转移到较小的队列数据集（例如，儿童脑肿瘤）。这种方法旨在从预先训练的模型中提取相关特征，解决带注释的儿科数据集的有限可用性，并增强儿童肿瘤的表征。讨论了这种方法在儿科神经肿瘤学中的含义和潜在应用。

Brain tumours are the most commonly occurring solid tumours in children, albeit with lower incidence rates compared to adults. However, their inherent heterogeneity, ethical considerations regarding paediatric patients, and difficulty in long-term follow-up make it challenging to gather large homogenous datasets for analysis. This study focuses on the development of a Convolutional Neural Network (CNN) for brain tumour characterisation using the adult BraTS 2020 dataset. We propose to transfer knowledge, from models pre-trained on extensive adult brain tumour datasets to smaller cohort datasets (e.g., paediatric brain tumours) in future studies, by leveraging Transfer Learning (TL). This approach aims to extract relevant features from pre-trained models, addressing the limited availability of annotated paediatric datasets and enhancing tumour characterisation in children. The implications and potential applications of this methodology in paediatric neuro-oncology are discussed.