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Neurosurgery Research Team
Neurosurgery research aims to improve treatments and functional outcomes for patients undergoing neurosurgery. We have randomised controlled trials for interventional treatments, acute and observational trials to determine the clinical effectiveness improve functional outcomes for neurosurgery patients.
Seizure PRophylaxis IN Glioma: A phase III randomised trial comparing prophylactic levetiracetam versus no prophylactic anti epileptic drug in patients with newly diagnosed presumed supratentorial cerebral glioma.
To determine ‘In seizure-naive newly diagnosed cerebral glioma patients undergoing surgery, does prophylactic levetiracetam pre-operatively and for at least 1 year, produce a meaningful (>50%) reduction in the risk of developing seizures, when compared with standard care (No AED)?’
Future GB Trial
The Future GB Trial is funded by the National Institute of Health Research (NIHR).
Stage 1 a non-randomised cohort study. It will evaluate standard care surgery with the addition of DTI imaging and the ultrasound imaging during guided resection of Glioblastoma.
Stage 2 is a randomised controlled trial. The trial plans to enrol 357 newly diagnosed patients to receive either brain surgery with standard methods without Ultrasound and Diffusion Tensor Imaging, or surgery with the addition of US and DTI as well as standard tools. The trial will result in only minor changes to the guided resection of Glioblastoma present care pathway.
Intervention: Surgery to resect the Glioblastoma using Diffusion Tensor Imaging and Navigated intraoperative ultrasound (where available) in addition to standard care.
The purpose of the MAST clinical trial is to assess:
- Anti-epileptic drug prophylaxis following a traumatic brain injury (MAST Prophylaxis).
- Duration of anti-epileptic drug treatment following post traumatic seizure (MAST Duration).
This will be assessed by conducting two parallel but independent trials.
Both studies will be preceded by an internal pilot in order to confirm recruitment, randomisation, treatment, and follow-up assessments. We have defined robust progression criteria, on the basis of recently published recommendations.
Improving treatment for Glioblastoma
Glioblastoma is the most aggressive kind of brain cancer and leads on average to 20 years of life lost, more than any other cancer. Images of the brain are taken before the operation, and every few months after treatment, to see if the cancer regrows.
The aim of this study is to provide doctors with a computer program that will use images of the brain that are routinely obtained throughout treatment, in order to help them more accurately identify when the cancer regrows. As well as being able to extract molecular information from MRI images, artificial intelligence techniques have also been shown to be effective in predicting response to treatment and also differentiating progression from pseudoprogression.
The aim of the research is to build a classification model that will be a predictive biomarker for pseudoprogression, using the type of routine MRI data already typically acquired during the care of glioblastoma patients.