AI-driven Processing and Analysis of Glioma Imaging Data (GLIOMAID)

July 9, 2026 updated by: Università degli Studi di Trento

AI-driven Processing and Analysis of Glioma Imaging Data EUCAIM Database Contribution by the Italian Brain Glioma Initiative Italian Title: Elaborazione ed Analisi Supportata Dall'Intelligenza Artificiale di Immagini di Risonanza Magnetica di Gliomi Cerebrali

GLIOMAID is a scientific research project focused on improving how brain tumors, specifically gliomas, are diagnosed and managed. It uses Artificial Intelligence (AI) to analyze MRI brain scans and patient data. The project collects existing clinical information and imaging from glioma patients to build AI models that support doctors in making better and faster treatment decisions.Gliomas, especially high-grade ones, are among the most common and challenging brain tumors. Many patients have poor survival chances, and diagnosis often requires invasive procedures like biopsies.

Despite medical advances, current treatments have limited effectiveness. Better non-invasive diagnostic tools are urgently needed to:

  • Detect tumors earlier.
  • Predict how aggressive they are.
  • Help doctors plan the most effective treatments. The GLIOMAID study aims to reduce the need for invasive diagnostics by creating AI tools that interpret brain scans with high accuracy.

Primary Objectives

  • Create Italy's First Glioma Imaging Database This database will store anonymized MRI scans and clinical records from around 700 patients.
  • Improve Early Detection Develop AI systems to identify brain tumors earlier from MRI scans.
  • Automate Tumor Mapping Use AI to outline tumors on MRI images to assist with surgical planning and treatment follow-up.
  • Non-Invasive Tumor Characterization Train AI models to predict tumor type and severity without needing a biopsy.

Secondary Objectives

  • Study how well AI tools fit into research and future clinical workflows.
  • Test how well AI can predict changes in tumors over time.

Lead Institution: University of Trento and Santa Chiara Hospital, Trento (Prof. Silvio Sarubbo, Principal Investigator).

Partner Hospitals: 7 neurosurgery and neuro-oncology centers across Italy.

Inclusion Criteria

  • Adults aged 18-60 with a confirmed glioma diagnosis (from 2019 to 2024).
  • Patients who had surgical tumor removal, with or without further treatment (e.g., chemotherapy, radiotherapy).
  • MRI scans and basic clinical data must be available.

Exclusion Criteria

  • Poor quality or incomplete MRI scans.
  • Missing essential clinical information.
  • If consent is explicitly refused (when it can be obtained).

Clinical Data

  • Age, sex, diagnosis date.
  • Tumor type and genetic information.
  • Treatments received (surgery, chemo, radiation).
  • Patient outcomes (e.g., survival, tumor progression).

Imaging Data

  • Pre- and post-operative MRI scans (T1, T2, FLAIR).
  • Segmented images highlighting tumor areas and post-surgery cavities.
  • Time points: before surgery, up to 6 months post-op, and during follow-up.

All data is pseudonymized (no personal identifiers) and securely stored.

Expected Results

  • Faster, more accurate diagnosis.
  • More personalized treatment planning.
  • Reduced need for invasive biopsies.

Benefits for Patients and Doctors Patients: Earlier diagnosis, less invasive procedures, better treatment outcomes.

Doctors: Improved decision-making tools, automated image analysis, consistent data for treatment planning.

Study Overview

Status

Recruiting

Conditions

Intervention / Treatment

Study Type

Observational

Enrollment (Estimated)

700

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Contact

Study Locations

  • Italy
      • Trento, Italy, 38122
        • Recruiting
        • CISMed, Centre for Medical Sciences
        • Contact:
        • Contact:

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

  • Adult

Accepts Healthy Volunteers

No

Sampling Method

Non-Probability Sample

Study Population

The study population consists of approximately 700 adult patients (100 per center) aged 18 to 60 years, diagnosed with brain glioma between 2019 and 2024 across seven specialized Italian neurosurgical centers. All participants underwent surgical tumor resection, with or without subsequent radiotherapy or chemotherapy. Only patients with high-quality MRI scans and essential clinical information are included. The study uses retrospective data, and where possible, informed consent is obtained. If consent cannot be collected due to patient death or unreachability, inclusion may still occur under ethically approved conditions. Data are pseudonymized and used to train and validate AI models.

Description

Inclusion Criteria:

  • Imaging (MRI) of confirmed glioma diagnosis in the period 2019-2024, for whom cancer types and stages, from diagnosis to post-treatment are available
  • Having undergone a full brain tumor resection operation, followed or not by treatment with RT or CHT
  • Adults aged 18 to 60 years
  • Informed consent available, when possible and applicable

Exclusion Criteria:

  • Poor quality or artifact-laden MRI images
  • Lack of a minimal set of clinical information
  • Explicit refusal of consent (if possible to obtain)
  • Age under 18 years or over 60 years

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

Cohorts and Interventions

Group / Cohort
Intervention / Treatment
Italian Glioma MRI Cohort (2019-2024) for AI-Based Detection and Characterization
This cohort comprises approximately 700 adult patients (aged 18-60) diagnosed with brain gliomas between 2019 and 2024 at seven high-expertise Italian neurosurgical centers. All patients underwent surgical resection, with or without subsequent chemotherapy or radiotherapy. The study collects retrospective clinical data (e.g., diagnosis, treatment history, outcomes) and MRI scans (pre- and post-operative). No new interventions are performed. Instead, the data is used to develop and validate AI models for early tumor detection, automated segmentation, and non-invasive histological characterization.
Other: AI-driven analysis of brain MRI data for early, non-invasive detection, segmentation, and histological characterization of gliomas using retrospective clinical and imaging records.
This intervention is distinguished by its focus on using AI algorithms-specifically convolutional neural networks (CNNs), recurrent neural networks (RNNs), and vision transformers (ViTs)-to analyze retrospective MRI data of glioma patients. Unlike prospective or interventional clinical trials, this study involves no new procedures or treatments; instead, it leverages existing imaging and clinical records to develop non-invasive tools for tumor detection, segmentation, and histological classification.

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Time Frame
Accuracy, sensitivity, specificity, and AUC of AI models for early glioma detection and classification from MRI, compared to expert evaluation and histological diagnosis.
Time Frame: Evaluation performed during the study period using retrospective MRI and clinical data collected from patients diagnosed between 2019 and 2024; AI model development and validation within 24 months.
Evaluation performed during the study period using retrospective MRI and clinical data collected from patients diagnosed between 2019 and 2024; AI model development and validation within 24 months.

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Università degli Studi di Trento

Publications and helpful links

The person responsible for entering information about the study voluntarily provides these publications. These may be about anything related to the study.

General Publications

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Actual)

January 21, 2026

Primary Completion (Estimated)

January 1, 2027

Study Completion (Estimated)

January 1, 2031

Study Registration Dates

First Submitted

July 9, 2026

First Submitted That Met QC Criteria

July 9, 2026

First Posted (Actual)

July 14, 2026

Study Record Updates

Last Update Posted (Actual)

July 14, 2026

Last Update Submitted That Met QC Criteria

July 9, 2026

Last Verified

April 1, 2026

More Information

Terms related to this study

Keywords

Additional Relevant MeSH Terms

Other Study ID Numbers

  • GLIOMAID

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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