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NCT Number: NCT07608003

Multicenter Prospective Study on MRI AI Model for Midline Glioma Subtyping and Prognosis:

A vision-language model using preoperative MRI and clinical variables has been developed to simultaneously predict three key molecular markers in midline gliomas: H3K27M, IDH, and 1p/19q. This prospective multicenter study will validate the model's accuracy in preoperative molecular subtyping and its value in prognostic assessment and clinical decision-making across multiple neurosurgical centers.

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Key information

About this study

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.

Who can participate

Only the study team can determine whether someone qualifies for participation.

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.

Treatment and study plan

Primary outcomes

  1. Diagnostic accuracy for midline glioma molecular subtypes

    Time frame: Perioperative

    Model predictions compared with postoperative histopathology and molecular testing (gold standard). Performance metrics include AUC, F1 score, sensitivity, specificity, and accuracy.

Study contacts

Contact information is provided by the study sponsor or research team.

Gong Xuan, MD.

CONTACT

[email protected]

0086-731-8975-3037

Shuwen Kuang, MD.

CONTACT

[email protected]

0086-13367494221

Sponsors and collaborators

Lead sponsor

Xiangya Hospital of Central South University

Other

Registry information

Official study title

Application of MRI-Based Artificial Intelligence Models for Preoperative Molecular Subtyping and Prognostic Assessment of Midline Gliomas: A Multicenter Prospective Clinical Study

Important dates

Study start
2026
Primary completion
2030
Study completion
2030
First posted
May 27, 2026
Registry last updated
May 27, 2026

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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