An observational study in Glioma and Glioma (Diagnosis), sponsored by Università degli Studi di Trento. Recruiting at 1 site in Italy. Open to participants aged 18 Years to 60 Years. Per ClinicalTrials.gov, last updated 2026-07-14.
Sponsored by Università degli Studi di Trento · Observational
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:
Primary Objectives
Secondary Objectives
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
Exclusion Criteria
Clinical Data
Imaging Data
All data is pseudonymized (no personal identifiers) and securely stored.
Expected Results
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.
1,397 studies on the registry are indexed under Glioma; 351 are open to participants now.
This study's planned enrollment of 700 is above the median of 88 across 238 observational studies indexed under Glioma.
Browse Glioma studies →Università degli Studi di Trento is the lead sponsor of 6 studies on the registry; 4 are open to participants now.
Counted across the registry records on this site, refreshed daily.
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.
Exclusion Criteria:
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.
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.
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Università degli Studi di Trento