GliomaAI-Oligo: MRI-Based Detection of IDH Mutant Oligodendroglioma

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

Completed

Looking for future studies?

Notify Me

Key information

Conditions

Glioma 1p19q Codeletion Astrocytoma Brain Diseases Brain Neoplasms Central Nervous System Diseases Central Nervous System Neoplasms Disease Glioma (Diagnosis) IDH-mutant Glioma (Oligodendroglioma, Astrocytoma) Neoplasms Neoplasms by Histologic Type Neoplasms by Site Neoplasms, Germ Cell and Embryonal Neoplasms, Glandular and Epithelial Neoplasms, Nerve Tissue Neoplasms, Neuroepithelial Nervous System Diseases Nervous System Neoplasms Neuroectodermal Tumors Oligodendroglioma Pathologic Processes Pathological Conditions, Signs and Symptoms

Age range

18 year and older

Sex eligibility

All sexes

Study type

Observational

Primary location

Deep Learning Institute of Radiological Sciences

Mumbai, India

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

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

Treatment and study plan

GliomaAI-Oligo

Device

Software as Medical Device (SaMD) for predicting Oligodendroglioma

Primary outcomes

Diagnostic performance of GliomaAI-Oligo for identification of IDH mutant Oligodendroglioma from MRI, measured by accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and area under the ROC

Time frame: Perioperative

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

Sponsors and collaborators

Lead sponsor

Deep Learning Institute of Radiological Sciences

Other

Registry information

Official study title

GliomaAI-Oligo: Non-Invasive MRI-Based Detection of IDH Mutant Oligodendroglioma Using Artificial Intelligence

Acronym: GliomaAI-Oligo

Important dates

Study start: 2017
Primary completion: 2021
Study completion: 2021
First posted: Jun 29, 2026
Registry last updated: Jun 29, 2026

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

Published trials that share one or more normalized conditions with this study.

Active, Not Recruiting