Clinical trial · Observational
Improving Treatment of Glioblastoma: Distinguishing Progression From Pseudoprogression
Improving Treatment of Glioblastoma by Distinguishing Progression From Pseudoprogression by Applying Machine Learning Techniques to Routine Clinical Data
- Source
- ClinicalTrials.gov
- Retrieved
- Sep 8, 2026
- Layer
- normalized (units and labels harmonized; values unchanged)
- Run
- ING-CLINICALTRIALS-20260908-000001
Summary
Brief summary (as posted)
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.
Conditions
Conditions (1)
Free-text conditions as registered, with the CancerIndex entity they were reconciled to and the match type.
| Condition (as posted) | Mapped entity | Match | Confidence |
|---|---|---|---|
| Glioblastoma | Glioblastoma | CURATED_BROADER | 0.80 |
Interventions
Interventions (0)
Data not yet available
Design
Arms and outcomes
Arms (0)
[]Primary outcomes (1)
- measure
- Accuracy of the artificial intelligence model
- timeFrame
- Up to 36 months
- description
- Defined by a confusion matrix of sensitivity and specificity to true positives and true negatives.
Secondary outcomes (1)
- measure
- Failure rate of the artificial intelligence model
- timeFrame
- Up to 36 months
- description
- The rate which the test cannot provide an outcome (e.g. due to poor quality or missing data)
Eligibility
Eligibility (as posted)
- Sex
- All
- Minimum age
- 18 Years
- Maximum age
- 80 Years
Show eligibility criteria text
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
References
Publications (0)
Data not yet available