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).

英国每年约有 25,000 名患者被诊断患有脑肿瘤。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。脑转移影响高达 40% 的颅外原发性癌症患者。脑肿瘤患者需要个体化的患者管理。脑肿瘤的自动检测和分割可以帮助个性化和标准化患者管理，并显着改善临床工作流程。我们之前开发了一个全自动人工智能框架，可以从 MRI 中分割前庭神经鞘瘤（一种脑肿瘤），从而达到最佳状态该项目将建立在最先进的成果之上。该项目旨在开发深度学习模型，以：1）使用以下方法检测并自动分割各种非神经胶质脑肿瘤（脑膜瘤、垂体腺瘤、脑转移瘤）核磁共振成像； 2) 开发综合临床和影像生物标志物来预测肿瘤生长和行为（垂体腺瘤、脑膜瘤）。