Improving Treatment of Glioblastoma: Distinguishing Progression From Pseudoprogression
- Conditions
- Glioblastoma
- Registration Number
- NCT04359745
- Lead Sponsor
- Guy's and St Thomas' NHS Foundation Trust
- Brief Summary
Glioblastoma is the most aggressive kind of brain cancer and leads on average to 20 years of life lost, more than any other cancer. MRI images of the brain are taken before the operation, and every few months after treatment, to see if the cancer regrows. It can be hard for doctors to tell if what they see in these images represent growing cancer or a sideeffect of treatment. The similarity of the appearance of the treatment side-effects to cancer is confusing and is known as "pseudoprogression" (as opposed to true cancer progression).
If doctors mistake the appearance of treatment side-effects for growing cancer, they may think that the treatment is failing and change the patient's treatment too early or put them into a clinical trial. This means that patients may not be given the full treatment and the results from some clinical trials cannot be trusted.
The aim of this study is to provide doctors with a computer program that will use MRI images of the brain that are routinely obtained throughout treatment, in order to help them more accurately identify when the cancer regrows.
- Detailed Description
The impact of pseudoprogression is significant on patient care and medical research. The existing evidence shows that it is feasible to use Support Vector Machine and Deep Learning classification models for predicting survival using routine MRI images as well as differentiating progression from pseudoprogression. The investigators wish to capture signal changing over time in routine MRI images using parametric response maps (via a state-of-the-art postoperative-to preoperative image registration method that they have developed) and use such classifiers to differentiate progression from pseudoprogression. The research the investigators are proposing is needed in order to provide a solution to the problem of pseudoprogression and be implemented across the NHS easily and efficiently. Importantly, this does not depend on advanced imaging techniques.
Data collected at KCH from the last 24 months shows that, even at a leading glioma imaging centre, only 66% of patients had advanced imaging (e.g. DSC-MRI) performed at the time of increase in contrast-enhancement i.e. possible progression. The primary aim of this research is to use routine clinical MRI data in order to train the classifier. This will increase the utility of the classifier, as such routine MRI data can be acquired by all imaging centres, and the new classifier can therefore provide a much more cost-efficient solution than an alternative classifier which may depend on advanced imaging techniques.
Initial training, testing and cross validation of a classification model will be carried out using MRI data of glioblastoma obtained from publicly-accessible imaging archives and King's College Hospital (KCH), London. For clinical validation, the trained model will undergo testing using MRI data from patients recruited prospectively.
Recruitment & Eligibility
- Status
- RECRUITING
- Sex
- All
- Target Recruitment
- 500
- Diagnosed with glioblastoma (World Health Organisation grade IV)
- Patient undergoing the standard Stupp treatment regimen
- Have had a pre-surgery scan and at least one follow-up scan post-chemoradiation
- Insufficient clinical and radiological follow-up
- The patient's treatment deviates greatly from the standard Stupp regimen, such as they are recruited into interventional trials and sufficient information is not known about the patient's trial treatment
- Patients receiving treatment with Angiogenesis inhibitors such as bevacizumab prior to completion of the Stupp regimen
Study & Design
- Study Type
- OBSERVATIONAL
- Study Design
- Not specified
- Primary Outcome Measures
Name Time Method Accuracy of the artificial intelligence model Up to 36 months Defined by a confusion matrix of sensitivity and specificity to true positives and true negatives.
- Secondary Outcome Measures
Name Time Method Failure rate of the artificial intelligence model Up to 36 months The rate which the test cannot provide an outcome (e.g. due to poor quality or missing data)
Trial Locations
- Locations (15)
Velindre Cancer Centre, Velindre University NHS Trust
🇬🇧Cardiff, United Kingdom
Ninewells Hospital and Medical School, NHS Tayside
🇬🇧Dundee, United Kingdom
Royal Sussex County Hospital, Brighton and Sussex University Hospitals NHS Trust
🇬🇧Brighton, United Kingdom
Leeds General Infirmary, The Leeds Teaching Hospitals NHS Trust
🇬🇧Leeds, United Kingdom
King's College Hospital, King's College Hospital NHS Trust
🇬🇧London, United Kingdom
Hull Royal Infirmary, Hull University Teaching Hospitals NHS Trust
🇬🇧Hull, United Kingdom
Charing Cross Hospital, Imperial College Healthcare NHS Trust
🇬🇧London, United Kingdom
Guy's Hospital, Guy's and St Thomas' NHS Foundation Trust
🇬🇧London, United Kingdom
National Hospital for Neurology and Neurosurgery, University College London Hospitals NHS Foundation Trust
🇬🇧London, United Kingdom
Nottingham University Hospitals NHS Trust- City Hospital
🇬🇧Nottingham, United Kingdom
University Hospitals Plymouth NHS Trust
🇬🇧Plymouth, United Kingdom
The Christie Hospital, The Christie NHS Foundation Trust
🇬🇧Manchester, United Kingdom
Newcastle upon Tyne Hospitals NHS Foundation Trust- Newcastle Freeman Hospital
🇬🇧Newcastle, United Kingdom
The Royal Marsden Hospital, Royal Marsden NHS Foundation Trust
🇬🇧Sutton, United Kingdom
Lancashire Teaching Hospitals NHS Foundation Trust
🇬🇧Preston, United Kingdom