Improving Treatment of Glioblastoma by Distinguishing Progression From Pseudoprogression by Applying Machine Learning Techniques to Routine Clinical Data
Trial ID: NCT04359745
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2019-03-21
Completion date: 2025-05-26
Sponsor: Guy's and St Thomas' NHS Foundation Trust
Collaborators: King's College London
Conditions: Glioblastoma
Locations: Royal Sussex County Hospital, Brighton and Sussex University Hospitals NHS Trust; Velindre Cancer Centre, Velindre University NHS Trust; Ninewells Hospital and Medical School, NHS Tayside; Hull Royal Infirmary, Hull University Teaching Hospitals NHS Trust; Leeds General Infirmary, The Leeds Teaching Hospitals NHS Trust; Guy's Hospital, Guy's and St Thomas' NHS Foundation Trust; King's College Hospital, King's College Hospital NHS Trust; Charing Cross Hospital, Imperial College Healthcare NHS Trust; National Hospital for Neurology and Neurosurgery, University College London Hospitals NHS Foundation Trust; The Christie Hospital, The Christie NHS Foundation Trust; Newcastle upon Tyne Hospitals NHS Foundation Trust- Newcastle Freeman Hospital; Nottingham University Hospitals NHS Trust- City Hospital; University Hospitals Plymouth NHS Trust; Lancashire Teaching Hospitals NHS Foundation Trust; The Royal Marsden Hospital, Royal Marsden NHS Foundation Trust
Countries: United Kingdom; United Kingdom; United Kingdom; United Kingdom; United Kingdom; United Kingdom; United Kingdom; United Kingdom; United Kingdom; United Kingdom; United Kingdom; United Kingdom; United Kingdom; United Kingdom; United Kingdom
Enrollment: 500
Age range: 18 Years - 80 Years
Sex: ALL

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.

Eligibility criteria:
Inclusion Criteria:

* 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

Exclusion Criteria:

* 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

Outcomes:
Accuracy of the artificial intelligence model