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

总结：脑肿瘤仍然是美国儿童癌症死亡的最常见原因，胶质瘤是35岁以下成年人癌症死亡的第二大原因。胶质瘤患者之间肿瘤特征的丰富多样性，即使在相同的肿瘤级别，是疾病结局预测的一大挑战。年龄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纳入了除组织病理学外的分子标记。这为识别复杂的“成像指纹”提供了机会，该指纹不仅可用于非侵入性基因分型，还可用于模拟特定年龄的肿瘤生长速度，预测肿瘤转化和脑侵袭。本研究的目的是整合神经肿瘤学的多模态数据，结合影像学、临床、病理和遗传变量，开发年龄认知、全自动、高维的脑肿瘤模型，用于个体水平的临床分诊、诊断、预后和治疗推断。

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.