Improving Treatment of Glioblastoma: Distinguishing Progression From Pseudoprogression

Glioblastoma Clinical Trial

Official title:

Improving Treatment of Glioblastoma by Distinguishing Progression From Pseudoprogression by Applying Machine Learning Techniques to Routine Clinical Data

Clinical Trial Details — Status: Recruiting

Administrative data

• NCT number: NCT04359745
• Other study ID #: 5.0 15/01/21
• Status: Recruiting
• Start date: March 21, 2019
• Est. completion date: May 26, 2025

Study information

• Verified date: April 2023
• Source: Guy's and St Thomas' NHS Foundation Trust
• Contact: Thomas C Booth, Dr
• Phone: 02078489568
• Email: thomas.booth[@]kcl.ac.uk
• Is FDA regulated: No
• Study type: Observational

Clinical 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. 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.

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 information / eligibility

• Status: Recruiting
• Enrollment: 500
• Est. completion date: May 26, 2025
• Est. primary completion date: May 26, 2024
• Accepts healthy volunteers: No
• Gender: All
• Age group: 18 Years to 80 Years

Eligibility

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

Related Conditions & MeSH terms

• Glioblastoma

Locations

Country	Name	City	State
United Kingdom	Royal Sussex County Hospital, Brighton and Sussex University Hospitals NHS Trust	Brighton
United Kingdom	Velindre Cancer Centre, Velindre University NHS Trust	Cardiff
United Kingdom	Ninewells Hospital and Medical School, NHS Tayside	Dundee
United Kingdom	Hull Royal Infirmary, Hull University Teaching Hospitals NHS Trust	Hull
United Kingdom	Leeds General Infirmary, The Leeds Teaching Hospitals NHS Trust	Leeds
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	King's College Hospital, King's College Hospital NHS Trust	London
United Kingdom	National Hospital for Neurology and Neurosurgery, University College London Hospitals NHS Foundation Trust	London
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	Nottingham University Hospitals NHS Trust- City Hospital	Nottingham
United Kingdom	University Hospitals Plymouth NHS Trust	Plymouth
United Kingdom	Lancashire Teaching Hospitals NHS Foundation Trust	Preston
United Kingdom	The Royal Marsden Hospital, Royal Marsden NHS Foundation Trust	Sutton

Sponsors (2)

Lead Sponsor	Collaborator
Guy's and St Thomas' NHS Foundation Trust	King's College London

Country where clinical trial is conducted

United Kingdom, 

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	Accuracy of the artificial intelligence model	Defined by a confusion matrix of sensitivity and specificity to true positives and true negatives.	Up to 36 months
Secondary	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)	Up to 36 months