Clinical trial · Observational
GliomaAI-MG: MRI-Based Detection of Molecular Glioblastoma
GliomaAI-MG: Non-Invasive MRI-Based Detection of Molecular Glioblastoma Using Artificial Intelligence
- 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)
The goal of this observational study is to learn whether an artificial intelligence system called GliomaAI-MG can help detect a specific molecular type of brain tumour called molecular glioblastoma using routine MRI scans. The study uses previously collected and fully anonymised MRI data from 1,372 patients from 13 institutions in the Cancer Imaging Archive (TCIA). The main questions it aims to answer are: * How accurately can GliomaAI-MG identify molecular glioblastoma from MRI scans? * How well does the system perform across data from different hospitals and patient groups? Researchers will use existing MRI scans and clinical information to train and test the AI system. No new scans, treatments, or hospital visits are required for participants, and all data used is fully anonymised and obtained from an existing research database. Participants will not be asked to do anything, as this study only uses previously collected imaging data.
Conditions
Conditions (3)
Free-text conditions as registered, with the CancerIndex entity they were reconciled to and the match type.
| Condition (as posted) | Mapped entity | Match | Confidence |
|---|---|---|---|
| Glioblastom WHO Grade 4 | Brain Neoplasm | PROBABILISTIC | 0.70 |
| Glioma | Glioma | ONTOLOGY_EXACT | 0.98 |
| Glioma (Diagnosis) | Glioma | ONTOLOGY_EXACT | 0.85 |
Interventions
Interventions (1)
| Intervention | Type | Mapped drug | Match |
|---|---|---|---|
| GliomaAI-MG | Device | — | UNRESOLVED |
Design
Arms and outcomes
Arms (0)
[]Primary outcomes (1)
- measure
- Diagnostic performance of GliomaAI-MG for identification of molecular glioblastoma from MRI, measured by accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and area under the receiver operating characteristic curve
- timeFrame
- Perioperative
- description
- The diagnostic performance of the GliomaAI-MG artificial intelligence model will be assessed by comparing pre-operative MRI-based predictions of molecular glioblastoma status against post-operative (biopsy or surgery) molecular/genetic profiling results as the reference standard. Performance metrics including accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and AUC will be calculated.
Eligibility
Eligibility (as posted)
- Sex
- All
- Minimum age
- 18 Years
Show eligibility criteria text
Inclusion Criteria: * Adult (\>=18 years of age) * Having pre op MRI scan * Having biopsy / surgery * Having post biopsy/ surgery histology diagnosis and genetic analysis. Exclusion Criteria: * MRI scan significantly degraded by motion or other artefact * Incomplete genetic analysis * Prior treatment (e.g., radiotherapy or chemotherapy) before baseline MRI
References
Publications (0)
Data not yet available