Age-cognizant, fully-automated, high-dimensional modelling of brain tumour appearances on MR imaging

MR 成像上脑肿瘤外观的年龄识别、全自动、高维建模

• 批准号: 2420555
• 金额: --
• 依托单位: University College London
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2019
• 资助国家: 英国
• 起止时间: 2019 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2420555
• 关键词: Age; cognizant; fully; automated; dimensional

Summary: Brain tumours remain the most common cause of cancer death in the US paediatric population and gliomas the second leading cause of cancer mortality in adults under 35. The rich diversity of tumour characteristics among glioma patients, even within the same tumour grade, is a big challenge for disease outcome prediction. Age-specific genetic differences are thought to account for the various clinical differences observed in malignant gliomas and the recent WHO reclassification of brain tumours incorporates, for the first time, molecular markers in addition to histopathology. This provides an opportunity to identify a complex "imaging fingerprint" that could not only be useful for non-invasive genotyping but also to model age-specific tumour growth rates, predict tumour transformation and brain invasion. The aim of this study is to integrate multimodal data in neuro-oncology, combining imaging, clinical, pathological and genetic variables to develop age-cognizant, fully-automated, high-dimensional modelling of brain tumours for the purpose of individual-level clinical triage, diagnosis, prognosis, and therapeutic inference.

总结：脑肿瘤仍然是美国儿科人群癌症死亡的最常见原因，神经胶质瘤是35岁以下成人癌症死亡的第二大原因。胶质瘤患者的肿瘤特征的丰富多样性，即使在相同的肿瘤等级内，也是疾病结局预测的一大挑战。脑胶质瘤特异性遗传差异被认为是恶性胶质瘤中观察到的各种临床差异的原因，最近世卫组织对脑肿瘤的重新分类首次将分子标记物与组织病理学结合起来。这提供了一个机会，以确定一个复杂的“成像指纹”，不仅可以用于非侵入性基因分型，但也建模年龄特异性肿瘤生长率，预测肿瘤转化和脑侵袭。本研究的目的是整合神经肿瘤学中的多模态数据，结合成像，临床，病理和遗传变量，以开发年龄识别，全自动，高维的脑肿瘤建模，用于个人水平的临床分诊，诊断，预后和治疗推断。