Improving Treatment of Glioblastoma: Distinguishing Progression From Pseudoprogression

April 20, 2023 updated by: Guy's and St Thomas' NHS Foundation Trust

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.

Study Overview

Status

Recruiting

Conditions

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 Type

Observational

Enrollment (Anticipated)

500

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Contact

Study Contact Backup

Study Locations

  • United Kingdom
      • Brighton, United Kingdom, BN2 5BE
        • Recruiting
        • Royal Sussex County Hospital, Brighton and Sussex University Hospitals NHS Trust
        • Principal Investigator:
          • Ed Chandy
      • Cardiff, United Kingdom, CF14 2TL
        • Recruiting
        • Velindre Cancer Centre, Velindre University NHS Trust
        • Principal Investigator:
          • James Powell
      • Dundee, United Kingdom, DD1 9SY
        • Recruiting
        • Ninewells Hospital and Medical School, NHS Tayside
        • Principal Investigator:
          • Avinash Kanodia
      • Hull, United Kingdom, HU3 2KZ
        • Recruiting
        • Hull Royal Infirmary, Hull University Teaching Hospitals NHS Trust
        • Principal Investigator:
          • Chris Rowland Hill
      • Leeds, United Kingdom, LS1 3EX
        • Recruiting
        • Leeds General Infirmary, The Leeds Teaching Hospitals NHS Trust
        • Principal Investigator:
          • Ryan Mathew
      • London, United Kingdom, W6 8RF
        • Recruiting
        • Charing Cross Hospital, Imperial College Healthcare NHS Trust
      • London, United Kingdom, SE1 9RT
        • Recruiting
        • Guy's Hospital, Guy's and St Thomas' NHS Foundation Trust
        • Principal Investigator:
          • Haris Shuaib
      • London, United Kingdom, SE5 9RS
        • Recruiting
        • King's College Hospital, King's College Hospital NHS Trust
      • London, United Kingdom, WC1N 3BG
        • Recruiting
        • National Hospital for Neurology and Neurosurgery, University College London Hospitals NHS Foundation Trust
        • Principal Investigator:
          • Steffi Thust
      • Manchester, United Kingdom, M20 4BX
        • Recruiting
        • The Christie Hospital, The Christie NHS Foundation Trust
        • Principal Investigator:
          • Sean Tenant
      • Newcastle, United Kingdom, NE7 7DN
        • Recruiting
        • Newcastle upon Tyne Hospitals NHS Foundation Trust- Newcastle Freeman Hospital
        • Principal Investigator:
          • Joanne Lewis
      • Nottingham, United Kingdom, NG7 2UH
        • Recruiting
        • Nottingham University Hospitals NHS Trust- City Hospital
      • Plymouth, United Kingdom, PL6 8DH
        • Recruiting
        • University Hospitals Plymouth NHS Trust
        • Principal Investigator:
          • Mark Thurston
      • Preston, United Kingdom, PR2 9HT
        • Recruiting
        • Lancashire Teaching Hospitals NHS Foundation Trust
        • Principal Investigator:
          • Erica Beamount
      • Sutton, United Kingdom, SM2 5PT
        • Recruiting
        • The Royal Marsden Hospital, Royal Marsden NHS Foundation Trust
        • Principal Investigator:
          • Liam Welsh

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

18 years to 80 years (Adult, Older Adult)

Accepts Healthy Volunteers

No

Sampling Method

Non-Probability Sample

Study Population

Patients with high-grade glioblastoma

Description

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

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

  • Observational Models: Cohort
  • Time Perspectives: Prospective

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
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.
Up to 36 months

Secondary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Failure rate of the artificial intelligence model
Time Frame: Up to 36 months
The rate which the test cannot provide an outcome (e.g. due to poor quality or missing data)
Up to 36 months

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Guy's and St Thomas' NHS Foundation Trust

Collaborators

King's College London

Investigators

  • Principal Investigator: Thomas Booth, King's College London

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Actual)

March 21, 2019

Primary Completion (Anticipated)

May 26, 2024

Study Completion (Anticipated)

May 26, 2025

Study Registration Dates

First Submitted

April 21, 2020

First Submitted That Met QC Criteria

April 21, 2020

First Posted (Actual)

April 24, 2020

Study Record Updates

Last Update Posted (Actual)

April 21, 2023

Last Update Submitted That Met QC Criteria

April 20, 2023

Last Verified

April 1, 2023

More Information

Terms related to this study

Keywords

Additional Relevant MeSH Terms

Other Study ID Numbers

  • 5.0 15/01/21

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

UNDECIDED

IPD Plan Description

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.

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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