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.
Age range
18 Years – 60 Years
Sex
ALL
See this in plain English?
AI-rewrites the medical criteria so a patient or caregiver can understand them. Always confirm with the trial site.
Bring these to your next appointment. They're a starting point for a shared conversation — not a sign you qualify or a recommendation to enrol.
Generated to help you prepare — always confirm anything about your own eligibility and care with the study team and your doctor.
The trial coordinator is the person who runs the study day to day. These cover the practical side — logistics, costs, and what taking part would actually mean for your life. The study team confirms whether you meet the criteria; these are questions to ask, not a sign you qualify.
A starting point for the conversation — always confirm anything about your own eligibility, costs, and care with the study team and your doctor.
Accuracy, sensitivity, specificity, and AUC of AI models for early glioma detection and classification from MRI, compared to expert evaluation and histological diagnosis.
Timeframe: 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.