AI-driven Processing and Analysis of Glioma Imaging Data (GLIOMAID)
2026年7月9日 更新者：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.
調査の概要
状態
募集
研究の種類
観察的
入学 (推定)
700
連絡先と場所
このセクションには、調査を実施する担当者の連絡先の詳細と、この調査が実施されている場所に関する情報が記載されています。
研究連絡先
- 名前：Silvio Sarubbo, MD Spec., PhD
- 電話番号：+ 39 0461 903487
- メール：silvio.sarubbo@unitn.it
研究場所
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Trento、イタリア、38122
- 募集
- CISMed, Centre for Medical Sciences
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コンタクト：
- Laura Barin, PhD in biostatistics
- 電話番号：+39 0461 283549
- メール：ricerca.cismed@unitn.it
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コンタクト：
- Paola Aronica, MSc in Pharmaceutics
- 電話番号：+39 0461 283544
- メール：paola.aronica@unitn.it
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参加基準
研究者は、適格基準と呼ばれる特定の説明に適合する人を探します。これらの基準のいくつかの例は、人の一般的な健康状態または以前の治療です。
適格基準
就学可能な年齢
- 大人
健康ボランティアの受け入れ
いいえ
サンプリング方法
非確率サンプル
調査対象母集団
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.
説明
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
研究計画
このセクションでは、研究がどのように設計され、研究が何を測定しているかなど、研究計画の詳細を提供します。
研究はどのように設計されていますか？
デザインの詳細
コホートと介入
グループ/コホート |
介入・治療 |
|---|---|
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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.
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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.
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この研究は何を測定していますか？
主要な結果の測定
結果測定 |
時間枠 |
|---|---|
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Accuracy, sensitivity, specificity, and AUC of AI models for early glioma detection and classification from MRI, compared to expert evaluation and histological diagnosis.
時間枠：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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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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協力者と研究者
ここでは、この調査に関係する人々や組織を見つけることができます。
出版物と役立つリンク
研究に関する情報を入力する責任者は、自発的にこれらの出版物を提供します。これらは、研究に関連するあらゆるものに関するものである可能性があります。
一般刊行物
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研究記録日
これらの日付は、ClinicalTrials.gov への研究記録と要約結果の提出の進捗状況を追跡します。研究記録と報告された結果は、国立医学図書館 (NLM) によって審査され、公開 Web サイトに掲載される前に、特定の品質管理基準を満たしていることが確認されます。
主要日程の研究
研究開始 (実際)
2026年1月21日
一次修了 (推定)
2027年1月1日
研究の完了 (推定)
2031年1月1日
試験登録日
最初に提出
2026年7月9日
QC基準を満たした最初の提出物
2026年7月9日
最初の投稿 (実際)
2026年7月14日
学習記録の更新
投稿された最後の更新 (実際)
2026年7月14日
QC基準を満たした最後の更新が送信されました
2026年7月9日
最終確認日
2026年4月1日
詳しくは
この情報は、Web サイト clinicaltrials.gov から変更なしで直接取得したものです。研究の詳細を変更、削除、または更新するリクエストがある場合は、register@clinicaltrials.gov。 までご連絡ください。 clinicaltrials.gov に変更が加えられるとすぐに、ウェブサイトでも自動的に更新されます。