Artificial intelligence-driven management of brain tumours

人工智能驱动的脑肿瘤管理

• 批准号: 2886747
• 金额: --
• 依托单位: King's College London
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2886747
• 关键词: Artificial; intelligence; driven; management; brain

Approximately 25,000 patients are diagnosed with a brain tumour every year in the UK. Meningiomas and pituitary adenomas are the first and third most common primary tumour, accounting for over 50% of all primary brain tumours. Brain metastases affect up to 40% of patients with extracranial primary cancer.Patients with brain tumours require individualized patient management. The automated detection and segmentation of brain tumours could help personalise and standardise patient management and significantly improve clinical workflow.We have previously developed a fully-automated AI framework to segment a vestibular schwannoma (a type of brain tumour) from MRI achieving state-of-the-art results on which this project will build on.This project aims to develop deep learning models to: 1) detect and automatically segment various non-glial brain tumours (meningioma, pituitary adenoma, brain metastases) using MRI; and 2) develop compositive clinical and imaging biomarkers to predict tumour growth and behaviour (pituitary adenoma, meningioma).

在英国，每年大约有25000名患者被诊断出患有脑瘤。脑膜瘤和垂体腺瘤是第一和第三常见的原发性肿瘤，占所有原发性脑肿瘤的50%以上。高达40%的颅外原发癌患者发生脑转移。脑肿瘤患者需要个体化治疗。脑肿瘤的自动检测和分割有助于患者管理的个性化和标准化，并显著改善临床工作流程。我们之前开发了一个全自动的人工智能框架，用于从MRI中分割前庭神经鞘瘤（一种脑肿瘤），获得了最先进的结果，该项目将以此为基础。本项目旨在开发深度学习模型：1)利用MRI检测和自动分割各种非胶质性脑肿瘤（脑膜瘤、垂体腺瘤、脑转移瘤）；2)开发综合临床和成像生物标志物来预测肿瘤的生长和行为（垂体腺瘤，脑膜瘤）。