Spots Global Cancer Trial Database for Hierarchical Diagnosis for Adult Diffuse Glioma Based on Deep Learning

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Trial Identification

Brief Title: Hierarchical Diagnosis for Adult Diffuse Glioma Based on Deep Learning

Official Title: Artificial Intelligence Research of Hierarchical Diagnosis for Adult Diffuse Glioma Based on Deep Learning

Study ID: NCT05624736

Conditions

Astrocytoma

Glioblastoma Multiforme

Oligodendroglioma

Interventions

multi-parametric magnetic resonance imaging scan

Pathology examination

Study Description

Brief Summary: This is a restrospective study to establish a deep learning model based on multi-parametric magnetic resonance imaging scans to predict Grade, histopathologic type and genotype of adult diffuse Glioma.

Detailed Description: Glioma is a common kind of tumor in central nervous system. The pre-operative prediction of grade, histopathologic type and genotype is important for treatment and management of Adult diffuse Glioma patients. Right now, most of the diagnostic prediction models on glioma are based on 2016 WHO central nervous system tumor guideline. The goal of this study is to establish a new deep learning model to predict Grade, histopathologic type and genotype of adult diffuse Glioma. We will recruit 500 patients with pathologically confirmed diagnosis of Glioblastoma, Astrocytoma and Oligodendroglioma who received neurologic surgery in our center. Each subject underwent pre-operative multi-parametric magnetic resonance imaging scans including T1WI, T2WI, T1CE, FLAIR and DWI. Pathologic diagnosis of each patient are available in pathology department. A deep learning based hierarchical diagnosis