GliomaGLIOMAIDNCT07703761
AI-driven Processing and Analysis of Glioma Imaging Data
An observational study of Glioma and Glioma (Diagnosis), run by Università degli Studi di Trento at 1 site in Italy.
Trial summary
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. Read more about this study
- Who is it for?
- Ages 18 to 60 years
- Conditions the study is for
- Glioma, Glioma (Diagnosis)
- What is being tested?
- AI-driven analysis of brain MRI data for early, non-invasive detection, segmentation, and histological characterization of gliomas using retrospective clinical and imaging records.
- How many people
- 700 participants
- Where
- 1 siteItaly
- Study dates
- 21 Jan 2026to 1 Jan 2031
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 to 60 years
- All sexes
- No healthy volunteers
- Adult
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.
Who may be able to join
- 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
Who cannot join
- 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
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
Italy1 site, 1 recruiting
- Recruiting
CISMed, Centre for Medical Sciences
Trento, 38122
Laura Barin, PhD in biostatistics, +39 0461 283549, ricerca.cismed@unitn.it
Paola Aronica, MSc in Pharmaceutics, +39 0461 283544, paola.aronica@unitn.it
About this study
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
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:
Read more
- 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.
- Conditions
- Glioma, Glioma (Diagnosis)
- Being tested
- AI-driven analysis of brain MRI data for early, non-invasive detection, segmentation, and histological characterization of gliomas using retrospective clinical and imaging records.
- Keywords
- Ai, clinical decision support, MRI, brain tumor, computer-assisted diagnosis, outcome prediction
- Medical subject headings
- Glioma, Disease, Brain Neoplasms
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
AI-driven analysis of brain MRI data for early, non-invasive detection, segmentation, and histological characterization of gliomas using retrospective clinical and imaging records.Other
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.
| Study group | Role | What they receive |
|---|---|---|
| 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. Receives: 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. |
What is being measured
The primary outcome is the main question the study is designed to answer.
Accuracy, sensitivity, specificity, and AUC of AI models for early glioma detection and classification from MRI, compared to expert evaluation and histological diagnosis.
Measured: 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.
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted14 Jul 2026
- Study start21 Jan 2026
- Primary completion1 Jan 2027 (estimated)
- Study completion1 Jan 2031 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (19)Papers the sponsor has linked to this study.
- Other trials for GliomaStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Retrospective
- Enrolment
- 700 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Università degli Studi di Trento
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07703761
- Sponsor's protocol ID
- GLIOMAID
- Acronym
- GLIOMAID
- Status last verified
- Apr 2026
- First submitted
- 9 Jul 2026
- Last updated
- 14 Jul 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
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.
PubMed 37979340Tomassini 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.
PubMed 35691714Suganyadevi 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.
PubMed 34513553Ruda 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 Section. Italian consensus and recommendations on diagnosis and treatment of low-grade gliomas. An intersociety (SINch/AINO/SIN) document. J Neurosurg Sci. 2020 Aug;64(4):313-334. doi: 10.23736/S0390-5616.20.04982-6. Epub 2020 Apr 29.
PubMed 32347684Kotrotsou 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.
PubMed 29619649Zigiotto 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.
PubMed 32303975Sanai 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.
PubMed 18496181Capelle 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 article. J Neurosurg. 2013 Jun;118(6):1157-68. doi: 10.3171/2013.1.JNS121. Epub 2013 Mar 15.
PubMed 23495881Chen D, Persson A, Sun Y, Salford LG, Nord DG, Englund E, Jiang T, Fan X. Better prognosis of patients with glioma expressing FGF2-dependent PDGFRA irrespective of morphological diagnosis. PLoS One. 2013 Apr 22;8(4):e61556. doi: 10.1371/journal.pone.0061556. Print 2013.
PubMed 23630597Brito C, Azevedo A, Esteves S, Marques AR, Martins C, Costa I, Mafra M, Bravo Marques JM, Roque L, Pojo M. Clinical insights gained by refining the 2016 WHO classification of diffuse gliomas with: EGFR amplification, TERT mutations, PTEN deletion and MGMT methylation. BMC Cancer. 2019 Oct 17;19(1):968. doi: 10.1186/s12885-019-6177-0.
PubMed 31623593Weller M, van den Bent M, Preusser M, Le Rhun E, Tonn JC, Minniti G, Bendszus M, Balana C, Chinot O, Dirven L, French P, Hegi ME, Jakola AS, Platten M, Roth P, Ruda R, Short S, Smits M, Taphoorn MJB, von Deimling A, Westphal M, Soffietti R, Reifenberger G, Wick W. EANO guidelines on the diagnosis and treatment of diffuse gliomas of adulthood. Nat Rev Clin Oncol. 2021 Mar;18(3):170-186. doi: 10.1038/s41571-020-00447-z. Epub 2020 Dec 8.
PubMed 33293629Ohgaki H. Epidemiology of brain tumors. Methods Mol Biol. 2009;472:323-42. doi: 10.1007/978-1-60327-492-0_14.
PubMed 19107440Lemee JM, Clavreul A, Menei P. Intratumoral heterogeneity in glioblastoma: don't forget the peritumoral brain zone. Neuro Oncol. 2015 Oct;17(10):1322-32. doi: 10.1093/neuonc/nov119. Epub 2015 Jul 22.
PubMed 26203067Ius T, Pignotti F, Della Pepa GM, La Rocca G, Somma T, Isola M, Battistella C, Gaudino S, Polano M, Dal Bo M, Bagatto D, Pegolo E, Chiesa S, Arcicasa M, Olivi A, Skrap M, Sabatino G. A Novel Comprehensive Clinical Stratification Model to Refine Prognosis of Glioblastoma Patients Undergoing Surgical Resection. Cancers (Basel). 2020 Feb 7;12(2):386. doi: 10.3390/cancers12020386.
PubMed 32046132Louis DN, Perry A, Wesseling P, Brat DJ, Cree IA, Figarella-Branger D, Hawkins C, Ng HK, Pfister SM, Reifenberger G, Soffietti R, von Deimling A, Ellison DW. The 2021 WHO Classification of Tumors of the Central Nervous System: a summary. Neuro Oncol. 2021 Aug 2;23(8):1231-1251. doi: 10.1093/neuonc/noab106.
PubMed 34185076
Study at a glance
From the registry record, last updated by the sponsor on 14 Jul 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- 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
- ClinicalTrials.gov ID
- NCT07703761
- Acronym / sponsor ID
- GLIOMAID / GLIOMAID
- Conditions
- Glioma, Glioma (Diagnosis)
- Keywords
- Ai, clinical decision support, MRI, brain tumor, computer-assisted diagnosis, outcome prediction
- Study type
- Observational
- Recruitment status
- Recruiting
- Enrolment
- 700 participants (estimated)
- Lead sponsor
- Università degli Studi di TrentoUniversity, hospital or other
- Study start
- 21 Jan 2026 (actual)
- Primary completion
- 1 Jan 2027 (estimated)
- Study completion
- 1 Jan 2031 (estimated)
- First posted
- 14 Jul 2026 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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 design
- Observational model
- Cohort
- Time perspective
- Retrospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Italian Glioma MRI Cohort (2019-2024) for AI-Based Detection and 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 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. |
Who may qualify
- Ages
- 18 to 60 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult
- 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.
Key inclusion criteria (4 listed)
- 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
Key exclusion criteria (4 listed)
- 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
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| 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. | Other | Italian Glioma MRI Cohort (2019-2024) for AI-Based Detection and Characterization |
Primary outcome
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.
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 14 Jul 2026.
- Background: PubMed 37979340Tomassini 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.
- Background: PubMed 35691714Tomassini 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.
- Background: PubMed 34513553Suganyadevi 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.
- Background: PubMed 32347684Ruda 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 Section. Italian consensus and recommendations on diagnosis and treatment of low-grade gliomas. An intersociety (SINch/AINO/SIN) document. J Neurosurg Sci. 2020 Aug;64(4):313-334. doi: 10.23736/S0390-5616.20.04982-6. Epub 2020 Apr 29.
- Background: PubMed 29619649Kotrotsou 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.
- Background: PubMed 32303975Zigiotto 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.
- Background: PubMed 18496181Sanai 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.
- Background: PubMed 23495881Capelle 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 article. J Neurosurg. 2013 Jun;118(6):1157-68. doi: 10.3171/2013.1.JNS121. Epub 2013 Mar 15.
- Background: PubMed 23630597Chen D, Persson A, Sun Y, Salford LG, Nord DG, Englund E, Jiang T, Fan X. Better prognosis of patients with glioma expressing FGF2-dependent PDGFRA irrespective of morphological diagnosis. PLoS One. 2013 Apr 22;8(4):e61556. doi: 10.1371/journal.pone.0061556. Print 2013.
- Background: PubMed 31623593Brito C, Azevedo A, Esteves S, Marques AR, Martins C, Costa I, Mafra M, Bravo Marques JM, Roque L, Pojo M. Clinical insights gained by refining the 2016 WHO classification of diffuse gliomas with: EGFR amplification, TERT mutations, PTEN deletion and MGMT methylation. BMC Cancer. 2019 Oct 17;19(1):968. doi: 10.1186/s12885-019-6177-0.
- Background: PubMed 33293629Weller M, van den Bent M, Preusser M, Le Rhun E, Tonn JC, Minniti G, Bendszus M, Balana C, Chinot O, Dirven L, French P, Hegi ME, Jakola AS, Platten M, Roth P, Ruda R, Short S, Smits M, Taphoorn MJB, von Deimling A, Westphal M, Soffietti R, Reifenberger G, Wick W. EANO guidelines on the diagnosis and treatment of diffuse gliomas of adulthood. Nat Rev Clin Oncol. 2021 Mar;18(3):170-186. doi: 10.1038/s41571-020-00447-z. Epub 2020 Dec 8.
- Background: PubMed 19107440Ohgaki H. Epidemiology of brain tumors. Methods Mol Biol. 2009;472:323-42. doi: 10.1007/978-1-60327-492-0_14.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Glioma and Glioma (Diagnosis), run by Università degli Studi di Trento at 1 site in Italy.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 4 inclusion and 4 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: looking for participants now, as of the sponsor's last update on 14 Jul 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Italy. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Silvio Sarubbo, MD Spec., PhD (+ 39 0461 903487, silvio.sarubbo@unitn.it), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
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.). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT07703761, which also lists 19 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 14 Jul 2026; AzTrials last refreshed its copy on 26 Oct 2026.