- ICH GCP
- Amerikanska kliniska prövningsregistret
- Klinisk prövning NCT07608003
Multicenter Prospective Study on MRI AI Model for Midline Glioma Subtyping and Prognosis:
Application of MRI-Based Artificial Intelligence Models for Preoperative Molecular Subtyping and Prognostic Assessment of Midline Gliomas: A Multicenter Prospective Clinical Study
Studieöversikt
Status
Detaljerad beskrivning
This study aims to validate the clinical value of an MRI-based artificial intelligence model for personalized diagnosis and treatment in patients with midline gliomas. The model integrates preoperative MRI features with clinical variables (e.g., age, sex, and other relevant patient characteristics) to predict both molecular subtypes and patient prognosis.
Model workflow. The model takes as input tumor-containing slices from preoperative MRI sequences, along with patient age and sex. By recognizing information within the MRI sequences, the model outputs the predicted molecular diagnosis for the patient.
Primary objective. To evaluate the model's accuracy in preoperative molecular subtyping of midline gliomas (H3K27M, IDH, and 1p/19q status) by comparing its predictions with the gold standard of postoperative or post-biopsy pathology. Diagnostic performance will be assessed using sensitivity, specificity, accuracy, F1 score, and area under the receiver operating characteristic curve (AUC).
Secondary objective. To assess the model's prognostic capability by integrating imaging features with clinical variables to predict patient survival outcomes and treatment response. Prognostic performance will be evaluated using time-dependent AUC and calibration metrics.
Exploratory objective. To explore the model's added value in clinical decision-making, including its potential to guide preoperative treatment planning and risk stratification.
This prospective, multicenter study will be conducted across several tertiary neurosurgical centers in China. The findings are expected to provide high-level evidence supporting non-invasive, precise diagnosis and personalized management of midline gliomas.
Studietyp
Inskrivning (Beräknad)
Kontakter och platser
Studiekontakt
- Namn: Gong Xuan, MD.
- Telefonnummer: 0086-731-8975-3037
- E-post: gong.xuan@csu.edu.cn
Studera Kontakt Backup
- Namn: Shuwen Kuang, MD.
- Telefonnummer: 0086-13367494221
- E-post: 228102171@csu.edu.cn
Studieorter
-
-
Hunan
-
Changsha, Hunan, Kina, 410008
- Rekrytering
- Xiangya Hospital of Central South University
-
Kontakt:
- Shuwen Kuang, MD.
- Telefonnummer: 0086-13367494221
- E-post: 228102171@csu.edu.cn
-
Kontakt:
- Xuan Gong, MD.
- Telefonnummer: 0086-731-8975-3037
- E-post: gong.xuan@csu.edu.cn
-
-
Deltagandekriterier
Urvalskriterier
Åldrar som är berättigade till studier
- Barn
- Vuxen
- Äldre vuxen
Tar emot friska volontärer
Testmetod
Studera befolkning
Beskrivning
Inclusion Criteria:
- Patients with diffuse gliomas were pathologically and molecularly diagnosed.
- The clinical case data of all patients were complete.
- Patients underwent preoperative MRI examination.
Exclusion Criteria:
- The tumor is not located in the intracranial midline.
- Cases in which MRI were incomplete or with significant noise and artifacts.
Studieplan
Hur är studien utformad?
Designdetaljer
Vad mäter studien?
Primära resultatmått
Resultatmått |
Åtgärdsbeskrivning |
Tidsram |
|---|---|---|
|
Diagnostic accuracy for midline glioma molecular subtypes
Tidsram: Perioperative
|
Model predictions compared with postoperative histopathology and molecular testing (gold standard).
Performance metrics include AUC, F1 score, sensitivity, specificity, and accuracy.
|
Perioperative
|
Samarbetspartners och utredare
Studieavstämningsdatum
Studera stora datum
Studiestart (Beräknad)
Primärt slutförande (Beräknad)
Avslutad studie (Beräknad)
Studieregistreringsdatum
Först inskickad
Först inskickad som uppfyllde QC-kriterierna
Första postat (Faktisk)
Uppdateringar av studier
Senaste uppdatering publicerad (Faktisk)
Senaste inskickade uppdateringen som uppfyllde QC-kriterierna
Senast verifierad
Mer information
Termer relaterade till denna studie
Ytterligare relevanta MeSH-villkor
Andra studie-ID-nummer
- 2026040726
Plan för individuella deltagardata (IPD)
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