Assessment of Brain Tumours Using Machine Learning and MRI Radiomics

该项目的目的是为临床医生构建一个基于人工智能的工具，用于评估脑肿瘤，以帮助制定治疗计划并为临床决策提供信息。这将对患者治疗结果产生重大影响（例如，减少不必要的侵入性手术数量）。该项目将使用机器学习来关联脑膜瘤的放射学，放射学和组织学发现，以黄金标准为基准，高度预测脑膜瘤组织的基因组数据，以开发更好的肿瘤分级和临床结果的非侵入性预测模型。这些模型将有助于临床决策的监督和监督间隔vssurgery +/-放疗。该项目将与普利茅斯脑肿瘤卓越中心合作开展，该中心拥有脑肿瘤样本和相关数据的生物库，包括基因组和MRI数据。这个跨学科项目汇集了脑肿瘤和神经成像方面的临床专业知识以及人工智能，软件开发和生物医学数据分析方面的专业知识。

The aim of the project is to build an AI-based tool for clinicians to use to assess brain tumours to help with treatment planning and to inform clinical decision-making. This will have a significant impact on patient treatment outcome (e.g. a reduction in the number of unnecessary invasive procedures). The project will use machine learning to correlate radiomic, radiological and histological findings in meningiomas, bench marked against the gold standard, highly predictive genomic data from meningioma tissue, in order to develop better non-invasive predictive models of tumour grade and clinical outcome. Such models would help with clinical decisions about surveillance and surveillance intervals vssurgery +/- radiotherapy. The project will be carried out in collaboration with the Plymouth Centre of Excellence for Brain Tumour which has a biobank of brain tumour samples and associated data, including genomic and MRI data. This interdisciplinary project brings together clinical expertise in brain tumour and neuro-imaging and expertise in AI, software development, and biomedical data analysis.