GliomaAI-GBM: MRI-Based Detection of IDH Wildtype Glioblastoma

NCT07673692 | Completed DMC FDA Device

• 1 other identifier: DLIRS-GLIOMAAI-GBM
• Study Type: observational
• Target: 1,372
• Locations: 1 country
• Sites: 1

Brief Summary

The goal of this observational study is to learn whether an artificial intelligence system called GliomaAI-GBM can help detect a specific molecular type of brain tumour called IDH wildtype 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-GBM identify IDH wildtype 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

Glioma; Glioma (Diagnosis); Glioblastom WHO Grade 4; IDH Wildtype Glioblastoma

Keywords

Glioma; Brain Tumour; Glioma AI; MRI; Histological Glioblastoma; IDH Wildtype Glioblastoma

Outcome Measures

Primary Outcomes (1)

• Diagnostic performance of GliomaAI-GBM for identification of IDH wildtype glioblastoma from MRI, measured by accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and area under the ROC.

  The diagnostic performance of the GliomaAI-GBM artificial intelligence model will be assessed by comparing pre-operative MRI-based predictions of IDH wildtype 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.

  Perioperative

Interventions

GliomaAI-GBM DEVICE

Software as Medical Device (SaMD) for predicting histological glioblastoma

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Patients with glioma diagnosis included in the TCIA cohort.

You may qualify if:

• Adult (\>=18 years of age)
• Having pre op MRI scan
• Having biopsy / surgery
• Having post biopsy/ surgery histology diagnosis and genetic analysis.

You may not qualify if:

• MRI scan significantly degraded by motion or other artefact
• Incomplete genetic analysis
• Prior treatment (e.g., radiotherapy or chemotherapy) before baseline MRI

Sponsors & Collaborators

• Deep Learning Institute of Radiological Sciences: lead

Study Sites (1)

Deep Learning Institute of Radiological Sciences

Mumbai, India

Location

MeSH Terms

Conditions

Glioma; Disease; Brain Neoplasms

Condition Hierarchy (Ancestors)

Neoplasms, Neuroepithelial; Neuroectodermal Tumors; Neoplasms, Germ Cell and Embryonal; Neoplasms by Histologic Type; Neoplasms; Neoplasms, Glandular and Epithelial; Neoplasms, Nerve Tissue; Pathologic Processes; Pathological Conditions, Signs and Symptoms; Central Nervous System Neoplasms; Nervous System Neoplasms; Neoplasms by Site; Brain Diseases; Central Nervous System Diseases; Nervous System Diseases

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: June 13, 2026
• First Posted: June 29, 2026
• Study Start: March 14, 2017
• Primary Completion: March 9, 2021
• Study Completion: July 16, 2021
• Last Updated: June 29, 2026

Record last verified: 2026-06

Data Sharing

• IPD Sharing: Will share
• Shared Documents: STUDY PROTOCOL
• Time Frame: Study protocol and methodology will be published in an academic journal
• Access Criteria: We will strive to publish it in the open access academic journal

Locations

IN (1)