Hierarchical Diagnosis for Adult Diffuse Glioma Based on Deep Learning

Status Recruiting

Clinical Trial 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.

Clinical Trial 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 ;

Study Design

Related Conditions & MeSH terms

Clinical Trial Details

NCT number	NCT05624736
Study type	Observational
Source	The Affiliated Nanjing Drum Tower Hospital of Nanjing University Medical School
Contact	Xin Zhang, Master
Phone	13814066403
Email	zhangxin@njglyy.com
Status	Recruiting
Phase
Start date	November 20, 2022
Completion date	May 1, 2025

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