Paediatric brain tumours and their treatments are associated with long-term cognitive impairment. While the aetiology of cognitive impairment is complex and multifactorial, multiparametric Magnetic Resonance Imaging (MRI) can identify many risk factors including tumour location, damage to eloquent structures and tumour phenotype. Hydrocephalus and raised intracranial pressure can be observed, along with risk factors for post-operative paediatric cerebellar mutism syndrome or epilepsy. MRI can also identify complications of surgery or radiotherapy and monitor treatment response. Advanced imaging sequences provide valuable information about tumour and brain physiology, but clinical use is limited by extended scanning times and difficulties in processing and analysis. Brain eloquence classifications exist, but focus on adults with neurological deficits and are outdated. For the analysis of childhood tumours, limited numbers within tumour subgroups and the investigation of long-term outcomes necessitate using historical scans and/or multi-site collaboration. Variable imaging quality and differing acquisition parameters limit the use of segmentation algorithms and radiomic analysis. Harmonisation can standardise imaging in collaborative research, but can be challenging, while data-sharing produces further logistical challenges. Consequently, most research consists of small single-centre studies limited to regional analyses of tumour location. Technological advances reducing scanning times increase the feasibility of clinical acquisition of high-resolution standardised imaging including advanced physiological sequences. The RAPNO and SIOPE paediatric brain tumour imaging guidelines have improved image standardisation, which will benefit future collaborative imaging research. Modern machine learning techniques provide more nuanced approaches for integration and analysis of the complex and multifactorial data involved in cognitive outcome prediction.
儿童脑肿瘤及其治疗与长期认知障碍相关。尽管认知障碍的病因复杂且涉及多因素，但多参数磁共振成像（MRI）能够识别多种风险因素，包括肿瘤位置、重要功能结构损伤及肿瘤表型。可观察到脑积水和颅内压升高，同时存在术后儿童小脑性缄默综合征或癫痫的风险因素。MRI还能识别手术或放疗并发症，并监测治疗反应。先进的成像序列提供了关于肿瘤和脑生理学的宝贵信息，但其临床应用受到扫描时间延长以及处理和分析困难的限制。现有脑功能分区分类主要针对存在神经功能缺损的成人，且已过时。对于儿童肿瘤的分析，由于肿瘤亚组样本量有限以及长期预后研究的需要，必须使用历史扫描数据和/或多中心合作。成像质量不一和采集参数差异限制了分割算法和影像组学分析的应用。标准化可在合作研究中统一成像规范，但实施难度较大，而数据共享则带来更多后勤挑战。因此，大多数研究仅限于对肿瘤位置进行区域性分析的小型单中心研究。技术进步缩短了扫描时间，提高了临床获取高分辨率标准化成像（包括先进生理序列）的可行性。RAPNO和SIOPE儿童脑肿瘤成像指南已改善图像标准化，这将有利于未来的合作成像研究。现代机器学习技术为整合和分析认知结果预测中涉及的复杂多因素数据提供了更精细的方法。
肿瘤(癌症)患者之家