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Inclusion Criteria:

• - Patients aged 18-70 years, male or female; - Patients with complete preoperative magnetic resonance imaging and were initially diagnosed with glioma based on the magnetic resonance imaging results; - Patients who were proposed for craniectomy without contraindications to surgery and who had signed an informed consent form by themselves or their proxy; - Patients with confirmed diagnosis of glioma by postoperative pathological diagnosis.

Exclusion Criteria:

• - Patients to be admitted for a simple puncture biopsy; - Patients with a combination of serious medical conditions and those who cannot tolerate surgery; - Patients with a bleeding tendency or abnormal coagulation function, or those who have not discontinued long-term use of anticoagulants such as aspirin and clopidogrel; - Pregnant and breastfeeding women; - Patients participate in other clinical trials during the same period.

BACKGROUND.The WHO 2016 officially introduced molecular markers into the pathological diagnosis of gliomas, marking a step into the era of molecular diagnosis of gliomas. Among them, isocitrate dehydrogenase 1 (IDH1) mutation is considered to be the 'backbone' in the development of gliomas, and affects the treatment plan and prognosis of patients. However, the clinical use of this molecular biomarker is still controversial, which is rooted in the lack of quantitative studies on IDH1 mutations. The spatial heterogeneity of gliomas has been demonstrated in existing studies, i.e., tumor tissues in different parts of the same glioma belong to different genetic subtypes. This implies that IDH1-mutant tumors do not indicate the presence of mutations in all tumor cells, thus further exacerbating the problems in clinical genetic diagnosis. OBJECTIVE.To quantify gene mutations in tumours, we plan to use radiomics model with artificial intelligence and clinical big data, and verify its accuracy by tissue puncture. In this way, we can overcome the challenges of multisite sampling and second-generation sequencing, such as high equipment and technology requirements, high cost and long time, and thus theoretically realise the visualisation and quantification of genetic heterogeneity within gliomas. PROCESS.Participants will read an informed consent agreement before surgery and voluntarily decide whether or not to join the experimental group. 1. Modelling of visualisation of genetic heterogeneity. Before surgery, participants first Routine imaging and the resulting images will be used to build a radiomics model. The model will non-invasively predict IDH1 mutations in gliomas. 2. Typical site puncture. After the enrolled participants were anaesthetised and craniotomised, clinicians selected typical tumor sites for puncture based on the model outputs. 3. Histopathological diagnosis. The specimen from the same puncture site is divided into two parts, and the first part is routinely formalin-fixed for paraffin embedding and finally H&E-stained sections. The pathologist first reads the H&E sections and makes a histological diagnosis, describing the pathological morphology and characteristics, especially the tumor cell content and distribution. 4. IDH1 single nucleotide sequencing. Another part of the sample is used for liquid nitrogen preservation. The Qiagen DNA/RNA Extraction Kit is used to extract DNA from the liquid nitrogen preserved tumour tissue, which is purified and subjected to the IDH1 polymerase chain reaction (PCR). The PCR product is purified and subjected to sequencing, and the sequencing product is detected on an ABI 7200 sequencer to determine whether IDH1 is mutated or not. 5. Mass spectrometry analysis of 2-Hydroxyglutarate (2-HG) expression levels. The presence and expression of 2-HG in glioma samples is detected and analysed by mass spectrometry. 6. Validation of the radiomics-based IDH1 mutation prediction model. The 2-HG detection results were numerically compared with the model results. This is a single centre validation study. Compared with the routine glioma surgical procedure, this study adds intraoperative tumor-typical sits puncture to validate the predictive accuracy of the radiomics model and collects corresponding MRI images, tumour histology diagnosis, molecular pathology diagnosis. The radiomics model is built based on preoperative clinical data and is a non-invasive and rapid tool for quantitative analysis and visualisation of tumor genes.

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