Artificial intelligence-driven management of brain tumours

在英国，每年大约有25,000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肿瘤，占所有原发脑肿瘤的50%以上。脑转移影响高达40%的颅外原发癌患者。患有脑肿瘤的患者需要个性化的患者管理。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，并显著改善临床工作流程。我们之前开发了一个全自动人工智能框架，从MRI中分割前庭神经鞘瘤(一种脑瘤)，并在此基础上取得最先进的结果。该项目旨在开发深度学习模型，以：1)使用MRI检测并自动分割各种非胶质脑瘤(脑膜瘤、垂体腺瘤、脑转移瘤)；以及2)开发综合的临床和成像生物标记物，以预测肿瘤的生长和行为(垂体腺瘤、脑膜瘤)。

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).