AI-driven Processing and Analysis of Glioma Imaging Data

Official title

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

Overview

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.

Interventions

• 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 classifica

Primary outcome measures

• 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.]

Eligibility criteria

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

Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.

Healthy volunteers: No

Study design

Observational model

Cohort

Study locations

Italy · 1 center

• CISMed, Centre for Medical Sciences — Trento

Publications

• Tomassini S, Falcionelli N, Bruschi G, Sbrollini A, Marini N, Sernani P, Morettini M, Muller H, Dragoni AF, Burattini L. On-cloud decision-support system for non-small cell lung cancer histology characterization from thorax computed tomography scans. Comput Med Imaging Graph. 2023 Dec;110:102310. doi: 10.1016/j.compmedimag.2023.102310. Epub 2023 Nov 10. PMID 37979340
• Tomassini S, Falcionelli N, Sernani P, Burattini L, Dragoni AF. Lung nodule diagnosis and cancer histology classification from computed tomography data by convolutional neural networks: A survey. Comput Biol Med. 2022 Jul;146:105691. doi: 10.1016/j.compbiomed.2022.105691. Epub 2022 Jun 6. PMID 35691714
• Suganyadevi S, Seethalakshmi V, Balasamy K. A review on deep learning in medical image analysis. Int J Multimed Inf Retr. 2022;11(1):19-38. doi: 10.1007/s13735-021-00218-1. Epub 2021 Sep 4. PMID 34513553
• Ruda R, Angileri FF, Ius T, Silvani A, Sarubbo S, Solari A, Castellano A, Falini A, Pollo B, Del Basso De Caro M, Papagno C, Minniti G, De Paula U, Navarria P, Nicolato A, Salmaggi A, Pace A, Fabi A, Caffo M, Lombardi G, Carapella CM, Spena G, Iacoangeli M, Fontanella M, Germano AF, Olivi A, Bello L, Esposito V, Skrap M, Soffietti R; SINch Neuro-Oncology Section, AINO and SIN Neuro-Oncology Sectio PMID 32347684
• Kotrotsou A, Elakkad A, Sun J, Thomas GA, Yang D, Abrol S, Wei W, Weinberg JS, Bakhtiari AS, Kircher MF, Luedi MM, de Groot JF, Sawaya R, Kumar AJ, Zinn PO, Colen RR. Multi-center study finds postoperative residual non-enhancing component of glioblastoma as a new determinant of patient outcome. J Neurooncol. 2018 Aug;139(1):125-133. doi: 10.1007/s11060-018-2850-4. Epub 2018 Apr 4. PMID 29619649
• Zigiotto L, Annicchiarico L, Corsini F, Vitali L, Falchi R, Dalpiaz C, Rozzanigo U, Barbareschi M, Avesani P, Papagno C, Duffau H, Chioffi F, Sarubbo S. Effects of supra-total resection in neurocognitive and oncological outcome of high-grade gliomas comparing asleep and awake surgery. J Neurooncol. 2020 May;148(1):97-108. doi: 10.1007/s11060-020-03494-9. Epub 2020 Apr 17. PMID 32303975
• Sanai N, Berger MS. Glioma extent of resection and its impact on patient outcome. Neurosurgery. 2008 Apr;62(4):753-64; discussion 264-6. doi: 10.1227/01.neu.0000318159.21731.cf. PMID 18496181
• Capelle L, Fontaine D, Mandonnet E, Taillandier L, Golmard JL, Bauchet L, Pallud J, Peruzzi P, Baron MH, Kujas M, Guyotat J, Guillevin R, Frenay M, Taillibert S, Colin P, Rigau V, Vandenbos F, Pinelli C, Duffau H; French Reseau d'Etude des Gliomes. Spontaneous and therapeutic prognostic factors in adult hemispheric World Health Organization Grade II gliomas: a series of 1097 cases: clinical articl PMID 23495881

Identifiers

NCT: NCT07703761 · GLIOMAID

Primary sources (government registries)

• ClinicalTrials.gov (US NIH)

View this study on ClinicalTrials.gov ↗