GlioblastomaNCT04359745
Improving Treatment of Glioblastoma: Distinguishing Progression From Pseudoprogression
An observational study of Glioblastoma, run by Guy's and St Thomas' NHS Foundation Trust at 15 sites in United Kingdom.
Trial 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. Read more about this study
- Who is it for?
- Ages 18 to 80 years
- Conditions the study is for
- Glioblastoma
- How many people
- 500 participants
- Where
- 15 sitesUnited Kingdom
- Study dates
- 21 Mar 2019to 26 May 2025
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 to 80 years
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: Patients with high-grade glioblastoma
Who may be able to join
- 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
Who cannot join
- 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
Trial locations and contacts
15 sites in 1 country.
Shuaib, MSc contact
15 sites in 1 country
United Kingdom15 sites, 15 recruiting
- Recruiting
Royal Sussex County Hospital, Brighton and Sussex University Hospitals NHS Trust
Brighton, BN2 5BE
Ed Chandy
- Recruiting
Velindre Cancer Centre, Velindre University NHS Trust
Cardiff, CF14 2TL
James Powell
- Recruiting
Ninewells Hospital and Medical School, NHS Tayside
Dundee, DD1 9SY
Avinash Kanodia
- Recruiting
Hull Royal Infirmary, Hull University Teaching Hospitals NHS Trust
Hull, HU3 2KZ
Chris Rowland Hill
- Recruiting
Leeds General Infirmary, The Leeds Teaching Hospitals NHS Trust
Leeds, LS1 3EX
Ryan Mathew
- Recruiting
Guy's Hospital, Guy's and St Thomas' NHS Foundation Trust
London, SE1 9RT
Haris Shuaib
- Recruiting
King's College Hospital, King's College Hospital NHS Trust
London, SE5 9RS
Thomas Booth
- Recruiting
Charing Cross Hospital, Imperial College Healthcare NHS Trust
London, W6 8RF
Matthew Williams
- Recruiting
National Hospital for Neurology and Neurosurgery, University College London Hospitals NHS Foundation Trust
London, WC1N 3BG
Steffi Thust
- Recruiting
The Christie Hospital, The Christie NHS Foundation Trust
Manchester, M20 4BX
Sean Tenant
- Recruiting
Newcastle upon Tyne Hospitals NHS Foundation Trust- Newcastle Freeman Hospital
Newcastle, NE7 7DN
Joanne Lewis
- Recruiting
Nottingham University Hospitals NHS Trust- City Hospital
Nottingham, NG7 2UH
Karen Foweraker
- Recruiting
University Hospitals Plymouth NHS Trust
Plymouth, PL6 8DH
Mark Thurston
- Recruiting
Lancashire Teaching Hospitals NHS Foundation Trust
Preston, PR2 9HT
Erica Beamount
- Recruiting
The Royal Marsden Hospital, Royal Marsden NHS Foundation Trust
Sutton, SM2 5PT
Liam Welsh
About this study
Official title: Improving Treatment of Glioblastoma by Distinguishing Progression From Pseudoprogression by Applying Machine Learning Techniques to Routine Clinical Data
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.
Read the 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.
- Conditions
- Glioblastoma
- Keywords
- MRI imaging, Glioblastoma, Glioma, Neoplasms
- Medical subject headings
- Glioblastoma, Glioma, Neoplasms
What is being measured
The primary outcome is the main question the study is designed to answer.
Accuracy of the artificial intelligence model
Defined by a confusion matrix of sensitivity and specificity to true positives and true negatives.
Measured: Up to 36 months
Secondary outcomes (1)
Failure rate of the artificial intelligence model
The rate which the test cannot provide an outcome (e.g. due to poor quality or missing data)
Measured: Up to 36 months
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted24 Apr 2020
- Study start21 Mar 2019
- Primary completion26 May 2024 (estimated)
- Study completion26 May 2025 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for GlioblastomaStudies of the same condition, open and under way.
- More trials run by Guy's and St Thomas' NHS Foundation TrustThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Prospective
- Enrolment
- 500 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Guy's and St Thomas' NHS Foundation Trust
- Sponsor type
- University, hospital or other
- Collaborators
- King's College London
- Responsible party
- Sponsor
- Investigators
- Thomas Booth, Principal investigator, King's College London
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT04359745
- Sponsor's protocol ID
- 5.0 15/01/21
- Status last verified
- Apr 2023
- First submitted
- 21 Apr 2020
- Last updated
- 21 Apr 2023
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Undecided: Fully anonymised datasets (including imaging and relevant clinical data) may be shared at the end of the study with public repositories for the purposes of furthering research and extending collaborations as long as all the regulations related to the database have been fully approved by the HRA and REC.
Study at a glance
From the registry record, last updated by the sponsor on 21 Apr 2023. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Improving Treatment of Glioblastoma by Distinguishing Progression From Pseudoprogression by Applying Machine Learning Techniques to Routine Clinical Data
- ClinicalTrials.gov ID
- NCT04359745
- Acronym / sponsor ID
- 5.0 15/01/21
- Conditions
- Glioblastoma
- Keywords
- MRI imaging, Glioblastoma, Glioma, Neoplasms
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 500 participants (estimated)
- Lead sponsor
- Guy's and St Thomas' NHS Foundation TrustUniversity, hospital or other
- Collaborators
- King's College London
- Study start
- 21 Mar 2019 (actual)
- Primary completion
- 26 May 2024 (estimated)
- Study completion
- 26 May 2025 (estimated)
- First posted
- 24 Apr 2020 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
Who may qualify
- Ages
- 18 to 80 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- Patients with high-grade glioblastoma
Key inclusion criteria (3 listed)
- 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
Key exclusion criteria (3 listed)
- 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
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
Accuracy of the artificial intelligence model
Time frame: Up to 36 months
Defined by a confusion matrix of sensitivity and specificity to true positives and true negatives.
1 secondary outcome
Failure rate of the artificial intelligence model
Time frame: Up to 36 months
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 21 Apr 2023.
- Individual participant dataSharing planned: Undecided. Fully anonymised datasets (including imaging and relevant clinical data) may be shared at the end of the study with public repositories for the purposes of furthering research and extending collaborations as long as all the regulations related to the database have been fully approved by the HRA and REC.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Shuaib, MSc contact
Thomas Booth principal investigator
King's College London
15 sites in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Glioblastoma, run by Guy's and St Thomas' NHS Foundation Trust at 15 sites in United Kingdom.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 3 inclusion and 3 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: status not confirmed, as of the sponsor's last update on 21 Apr 2023. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 15 sites in United Kingdom. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
Contact Thomas C Booth, Dr (02078489568, thomas.booth@kcl.ac.uk), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Accuracy of the artificial intelligence model (time frame: Up to 36 months). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 21 Apr 2023; AzTrials last refreshed its copy on 7 Nov 2026.