Data-driven composite biomarker development for the personalised management of vestibular schwannoma

数据驱动的复合生物标志物开发，用于前庭神经鞘瘤的个性化管理

• 批准号: 2606293
• 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: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2606293
• 关键词: Data; driven; composite; biomarker; development

Vestibular schwannoma (VS) is a benign, non-cancerous tumour arising from one of the balance nerves connecting the brain and inner ear. At current rates, approximately 1 in 1000 people will be diagnosed with a VS in their lifetime. For patients with smaller tumours, lifelong observation with repeated scans is advised but timely treatment is crucial in patients with growing tumours. We have previously developed a fully-automated AI framework to segment VS using MRI achieving state-of-the-art results. Aim of the investigation:State primary research question and where appropriate the primary hypotheses being tested This project aims to develop a deep learning model to predict which tumours are likely to grow and require treatment. This will enable clinicians to deliver personalised and standardised management plans to individual patients and has the potential to significantly reduce the number of required surveillance scans.

前庭神经鞘瘤（VS）是一种良性、非癌性肿瘤，起源于连接大脑和内耳的平衡神经之一。按照目前的速度，大约每1000人中就有1人在一生中被诊断出患有VS。对于肿瘤较小的患者，建议终身观察并重复扫描，但对于肿瘤生长的患者，及时治疗至关重要。我们之前已经开发了一个全自动的AI框架，使用MRI分割VS，实现了最先进的结果。调查目的：陈述主要研究问题，并在适当情况下测试主要假设该项目旨在开发一个深度学习模型，以预测哪些肿瘤可能生长并需要治疗。这将使临床医生能够为个体患者提供个性化和标准化的管理计划，并有可能显著减少所需的监测扫描次数。