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Trial Title:
Hierarchical Diagnosis for Adult Diffuse Glioma Based on Deep Learning
NCT ID:
NCT05624736
Condition:
Astrocytoma
Glioblastoma Multiforme
Oligodendroglioma
Conditions: Official terms:
Glioblastoma
Astrocytoma
Oligodendroglioma
Conditions: Keywords:
Astrocytoma
Glioblastoma
Oligodendroglioma
deep learning
artificial intelligence
Study type:
Observational
Overall status:
Recruiting
Study design:
Time perspective:
Retrospective
Intervention:
Intervention type:
Diagnostic Test
Intervention name:
multi-parametric magnetic resonance imaging scan
Description:
Pre-operative multi-parametric magnetic resonance imaging scans including T1WI, T2WI,
T1CE, FLAIR and DWI were taken for clinical needs.
Arm group label:
Astrocytoma Group
Arm group label:
Glioblastoma Group
Arm group label:
Oligodendroglioma Group
Intervention type:
Diagnostic Test
Intervention name:
Pathology examination
Description:
The tumor specimen obtained from the surgery were sent to the pathology department for
histopathologic examination, immunohistochemistry and gene sequencing test
Arm group label:
Astrocytoma Group
Arm group label:
Glioblastoma Group
Arm group label:
Oligodendroglioma Group
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
Criteria for eligibility:
Study pop:
Patients with diagnosis of adult diffuse glioma who recevived tumor rection surgery in
our center
Sampling method:
Probability Sample
Criteria:
Inclusion Criteria:
1. Patients undergoing surgery in Nanjing DrumTower Hospital between 2010.01 and
2022.05 with post-surgery pathological diagnosis of WHO Grade II to IV Adult Diffuse
Glioma.
2. Available pre-surgery T1WI, T2WI, T1CE, FLARI and DWI MR sequences
3. No pre-surgery anti-tumor therapy
Exclusion Criteria:
1. Poor image quality
2. Failed image preprocessing
3. Unavailable pathology data
Gender:
All
Minimum age:
18 Years
Maximum age:
90 Years
Healthy volunteers:
No
Locations:
Facility:
Name:
Department of Radiology, the Affiliated Drum Tower Hospital of Nanjing University
Address:
City:
Nanjing
Zip:
210093
Country:
China
Status:
Recruiting
Contact:
Last name:
Xin Zhang, Master
Phone:
13814066403
Email:
zhangxin@njglyy.com
Start date:
November 20, 2022
Completion date:
May 1, 2025
Lead sponsor:
Agency:
The Affiliated Nanjing Drum Tower Hospital of Nanjing University Medical School
Agency class:
Other
Source:
The Affiliated Nanjing Drum Tower Hospital of Nanjing University Medical School
Record processing date:
ClinicalTrials.gov processed this data on November 12, 2024
Source: ClinicalTrials.gov page:
https://clinicaltrials.gov/ct2/show/NCT05624736
