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| Name | Class |
|---|---|
| Sun Yat-sen University | OTHER |
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This registry aims to collect clinical, molecular and radiologic data including detailed clinical parameters, molecular pathology (1p/19q co-deletion, MGMT methylation, IDH and TERTp mutations, etc) and conventional/advanced/new MR sequences (T1, T1c, T2, FLAIR, ADC, DTI, PWI, etc) of patients with primary gliomas. By leveraging artificial intelligence, this registry will seek to construct and refine algorithms that able to predict molecular pathology or subgroups of gliomas.
Non-invasive and precise prediction for molecular biomarkers such as 1p/19q co-deletion, MGMT methylation, IDH and TERTp mutations is challenging. With the development of artificial intelligence, much more potential lies in the preoperative conventional/advanced MR imaging (T1 weighted imaging, T2 weighted imaging, FLAIR, contrast-enhanced T1 weighted imaging, diffusion-weighted imaging, and perfusion imaging) could be excavated to aid prediction of molecular pathology of gliomas. The creation of a registry for primary glioma with detailed molecular pathology, radiological data and with sufficient sample size for deep learning (>1000) provide considerable opportunities for personalized prediction of molecular pathology with non-invasiveness and precision.
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| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Prediction of molecular pathology | Diagnostic Test | Prediction of 1p/19q co-deletion, MGMT methylation, IDH and TERTp mutations or molecular subgroups by leveraging AI |
| Measure | Description | Time Frame |
|---|---|---|
| AUC of prediction performance | AUC=sensitivity+specificity-1 | up to 10 years |
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Inclusion Criteria:
Exclusion Criteria:
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Patients with newly diagnosed glioma that receive tumor resection
| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| Zhenyu Zhang, Dr. | Contact | +86 17839973727 | fcczhangzy1@zzu.edu.cn |
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| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Department of Neurosurgery, First Affiliated Hospital of Zhengzhou University | Recruiting | Zhengzhou | Henan | 450052 | China |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 37697238 | Derived | Liu Z, Hong X, Wang L, Ma Z, Guan F, Wang W, Qiu Y, Zhang X, Duan W, Wang M, Sun C, Zhao Y, Duan J, Sun Q, Liu L, Ding L, Ji Y, Yan D, Liu X, Cheng J, Zhang Z, Li ZC, Yan J. Radiomic features from multiparametric magnetic resonance imaging predict molecular subgroups of pediatric low-grade gliomas. BMC Cancer. 2023 Sep 11;23(1):848. doi: 10.1186/s12885-023-11338-8. |
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Undecided.
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| ID | Term |
|---|---|
| D005910 | Glioma |
| ID | Term |
|---|---|
| D018302 | Neoplasms, Neuroepithelial |
| D017599 | Neuroectodermal Tumors |
| D009373 | Neoplasms, Germ Cell and Embryonal |
| D009370 | Neoplasms by Histologic Type |
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All participants have signed the informed consent. Fresh frozen tissues of participants are collected immediately after tumor resection and preserved in liquid nitrogen. Whole exome sequencing, RNA sequencing and proteomics are planed to be conducted.
| D009369 | Neoplasms |
| D009375 | Neoplasms, Glandular and Epithelial |
| D009380 | Neoplasms, Nerve Tissue |