Glioma is the most common primary intracranial tumor in adults, with high-grade gliomas accounting for more than 50% of all gliomas and having the highest mortality rate. The molecular characteristics of gliomas are closely associated with the selection of treatment regimens, therapeutic efficacy, and patient prognosis. Mutations in isocitrate dehydrogenase (IDH) are an important diagnostic marker for diffuse gliomas and a key component in distinguishing and defining glioma subtypes. Furthermore, the advent of IDH-targeting inhibitors has brought hope to patients with IDH-mutant gliomas. Therefore, accurately assessing the IDH mutation status of gliomas is a prerequisite and key to precision diagnosis, targeted therapy, and prognostic evaluation. This study explores the application value of targeted IDH PET/MRI imaging in glioma subtype diagnosis, tumor grading, and prognostic evaluation. It aims to establish a non-invasive in vivo tracing technology platform and evaluation system for accurately assessing glioma IDH mutation status, subtype diagnosis, and prognostic evaluation. This will provide a basis for the precise diagnosis of gliomas, the selection of targeted treatment regimens, and prognostic assessment, and promote future research and development of therapeutic drugs using labeled therapeutic radionuclides.
Patient ≥ 18 years of age at the time of consent;
Patients with suspected, newly diagnosed, or previously treated gliomas;
Provided written informed consent authorisation before participating in the study.
Pregnancy or lactation;
MRI contraindications, not limited to cardiac implantable electronic devices and claustrophobia;
Participants with radiation exposure exceeding 50 mSv in the past year.
The subjects the investigators selected are adults who are not restricted by gender.
Case-only
Prospective
Targeted IDH PET/MRI
Diagnostic testPatients with clinical suspected gliomas will receive the targeted IDH PET/MRI.
Diagnostic efficacy, survival analysis
sensitivity, specificity, accuracy, positive and negative predictive values, ROC curve analysis,
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