AI-driven management of brain tumours (AIMBraTs)
Research type
Research Study
Full title
AI-driven management of brain tumours (AIMBraTs)
IRAS ID
320754
Contact name
Jonathan Shapey
Contact email
Sponsor organisation
King's College London
Duration of Study in the UK
0 years, 10 months, 31 days
Research summary
This study will analyse imaging and clinical data from patients with three different types of brain tumour (brain metastases, meningiomas, and non functioning pituitary adenomas). It is a retrospective study and will not affect patient treatment.
Approximately 25,000 patients are diagnosed with a brain tumour every year in the UK. Meningiomas (typically benign brain tumours originating from meninges – a three-layered membrane covering the brain and the spinal cord) and pituitary adenomas (benign tumours of the pituitary gland) are the first and third most common primary tumours, accounting for over 50% of all brain tumours. Brain metastases affect up to 40% of patients with extra-cranial primary cancer (tumours in the brain that have spread from primary cancers elsewhere in the body) and although there is presently no reliable data, metastatic brain tumours are thought to outnumber primary malignant brain tumours by at least 3:1
Patients with brain tumours require individualised patient management and may include surgery, radiation treatment and chemotherapy, either alone or in combination. Furthermore, for some patients with a benign (non-cancerous) brain tumour, such as those with smaller non-functioning pituitary adenoma (NFPA) or meningioma, a period of surveillance with regular scans is frequently performed before tumour resection is indicated. Lifelong scanning follow-up is usually required for most brain tumour patients.
We believe that Artificial Intelligence (AI)I can be programmed to detect these brain tumours from MRI scans. This project aims to develop a new state-of-the-art AI algorithm to automatically detect and classify three types of brain tumours.
REC name
North of Scotland Research Ethics Committee 1
REC reference
22/NS/0160
Date of REC Opinion
6 Dec 2022
REC opinion
Favourable Opinion