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Completed OBSERVATIONAL NCT07673692

Medical-AI trial: non-invasive AI detection of IDH-wildtype glioblastoma from MRI (GliomaAI-GBM, brain tumor, 1,372 public scans) (NCT07673692)

Deep Learning Institute of Radiological Sciences ・Updated 2026-06-29

An observational study evaluating whether an AI system, GliomaAI-GBM, can identify the molecular subtype IDH-wildtype glioblastoma from routine MRI. It uses 1,372 fully anonymized scans from 13 institutions in the Cancer Imaging Archive (TCIA) and measures accuracy, sensitivity, specificity, and area under the ROC curve. Completed.

Trial overview (primary data)

  • StatusCompleted
  • ConditionsGlioma, Glioma (Diagnosis), Glioblastom WHO Grade 4, IDH Wildtype Glioblastoma
  • InterventionsDEVICE: GliomaAI-GBM
  • SponsorDeep Learning Institute of Radiological Sciences
  • Target enrollment1,372 participants
  • Period2017-03-14 〜 2021-07-16

Key points

  • An observational study of whether AI (GliomaAI-GBM) can non-invasively identify IDH-wildtype glioblastoma from routine MRI.
  • Data are 1,372 anonymized MRI scans from 13 institutions in the Cancer Imaging Archive (TCIA); retrospective, with no new scans, treatment, or visits.
  • Primary endpoint is diagnostic performance (accuracy, sensitivity, specificity, predictive values, area under the ROC), with generalization across hospitals and patient groups.
  • IDH status is a key marker for glioma classification, prognosis, and treatment, but normally needs biopsy and molecular testing.
  • With sibling studies (astrocytoma, oligodendroglioma) it spans the major subtypes; it does not establish clinical use or biopsy replacement.
  • IDH status ordinarily requires tissue, while this assesses generalisation across 13 sites and 1,372 cases from a public database.

1Glioblastoma and molecular subtype

Glioblastoma is among the most aggressive brain tumors. In current classification, whether a tumor carries a mutation in the IDH gene (IDH-mutant) or not (IDH-wildtype) is a key marker that shapes the diagnosis, prognosis, and treatment plan. This subtype is normally determined by biopsy and subsequent molecular testing, but brain biopsy carries burden and risk.

If AI could estimate the subtype from MRI images alone, it might provide information earlier and less invasively.

2Can MRI tell them apart noninvasively

This trial applies AI (GliomaAI-GBM) to that non-invasive task as an observational study.

Per the registry summary, it uses anonymized MRI and clinical information from 1,372 patients across 13 institutions in the Cancer Imaging Archive (TCIA), a public research database, to measure how accurately GliomaAI-GBM identifies IDH-wildtype glioblastoma (accuracy, sensitivity, specificity, positive/negative predictive value, area under the ROC curve) and how well it generalizes across different hospitals and patient groups.

It is retrospective, asks nothing of participants, and uses only fully anonymized images. The same research program also registers sibling studies for astrocytoma (grades 2, 3, and 4) and oligodendroglioma, covering the major molecular subtypes of glioma with MRI and AI.

3Taking tissue, or estimating from an image

IDH status is central to classifying and prognosticating glioma, while confirming it ordinarily requires tissue. This study seeks the information a step earlier.

Determined by biopsy and molecular testingEstimated from MRI by AI
Yields a definitive diagnosisGives information early with less invasion
Brain biopsy carries burden and riskNo new imaging, treatment or visit is required of participants
Depends on local conditionsGeneralisation assessed across 13 sites and 1,372 cases

The data are anonymised MRI and clinical information from a public research database, the Cancer Imaging Archive. This is an instance of radiogenomics — reading genetic features from radiological images — and looking at generalisation across multiple sites marks a design with implementation in view. Sister studies on astrocytoma and oligodendroglioma are registered under the same programme.

Why it matters

Radiogenomics, estimating genetic features from radiology images, is an area expected to reduce invasiveness and yield earlier information. A design that evaluates generalization across multiple institutions is a useful reference for deploying and evaluating imaging AI (this trial is a retrospective performance study, not an establishment of clinical use).

FAQ

Why does IDH status (wildtype vs. mutant) matter?
In current brain-tumor classification, the presence or absence of an IDH mutation shapes the diagnosis, prognosis, and treatment plan. Glioblastoma is largely IDH-wildtype and is normally confirmed by tissue testing.
Can MRI alone replace a biopsy?
No. This is a retrospective study measuring AI identification performance on existing MRI; it does not replace biopsy or molecular testing, nor establish clinical use.

Related explainers

Sources (primary)

Source: ClinicalTrials.gov (U.S. NIH/NLM, public domain). This site does not provide medical advice. Verify the latest and exact details with the official source. This site is not endorsed or certified by the NIH/NLM.

#Medical AI#Clinical trial#MRI#Brain tumor#Radiogenomics#Imaging

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