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.

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.

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.

Patients with diffuse gliomas located in the intracranial midline.