Glioma Patients Registry Based on Radiological, Histopath... | NCT04220424

Studies
Glioma Patients Registry Based on Radiological, Histopathological and Genetic Analysis

NCT04220424

Sponsor

The First Affiliated Hospital of Zhengzhou University

Status

Unknown status

Last Update Posted

Feb 8, 2021Actual

Enrollment

500Estimated

Phase

Not provided

Conditions

Glioma

Interventions

MR and Histopathology images based prediction of molecular pathology and patient survival

Countries

China

Protocol Section

Identification Module Status Module Sponsor/Collaborators Module Oversight Module Description Module Conditions Module Design Module Arms/Interventions Module Outcomes Module Eligibility Module Contacts/Locations Module References Module IPD Sharing Statement Module

Results Section

Participant Flow Module Baseline Characteristics Module Outcome Measures Module Adverse Events Module More Info Module

Annotation Section

Annotation Module

Document Section

Large Document Module

Derived Section

Miscellaneous Info Module Condition Browse Module Intervention Browse Module

Protocol Section

Identification Module

NCT ID

Results Section

No data available

No data is available for this block.

Annotation Section

No data available

No data is available for this block.

Document Section

No data available

No data is available for this block.

Derived Section

Miscellaneous Info Module

Version Holder

Status Module

Record Verification Date

Feb 2021

Overall Recruitment Status or Expanded Access Status

Unknown status

Last Known Status

Recruiting

Delayed Posting

Not provided

Why Stopped

Not provided

Expanded Access Info

Start Date

Nov 1, 2018Actual

Primary Completion Date

Jan 1, 2022Estimated

Completion Date

Mar 1, 2022Estimated

First Submitted Date

Jan 4, 2020

First Submission Date that Met QC Criteria

Jan 4, 2020

First Posted Date

Jan 7, 2020Actual

Results Waived

Not provided

Results First Submitted Date

Not provided

Results First Submitted that Met QC Criteria

Not provided

Results First Posted Date

Not provided

Certification/Extension (aka Delayed Results) First Submitted Date

Not provided

Certification/Extension First Submitted that Passed QC Review

Not provided

Certification/Extension First Posted Date

Not provided

Last Update Submitted Date

Feb 5, 2021

Last Update Posted Date

Feb 8, 2021Actual

Sponsor/Collaborators Module

Responsible Party, by Official Title

Zhenyu Zhang, Principal Investigator, The First Affiliated Hospital of Zhengzhou UniversityPrincipal Investigator

Lead Sponsor

The First Affiliated Hospital of Zhengzhou UniversityOTHER

Collaborators

Name	Class
Sun Yat-sen University	OTHER

Oversight Module

Has Data Monitoring Committee (DMC)

Yes

Is FDA Regulated Drug

Is FDA Regulated Device

Is Unapproved Device

Not provided

Pediatric Postmarket Surveillance of a Device Product

Not provided

Product Exported from US

Not provided

FDAAA801 Violation

Not provided

Description Module

Brief Summary

This prospective study aims to collect clinical, radiological, pathological, molecular and genetic data including detailed clinical parameters, MR and histopathology images, molecular pathology and genetic sequencing data. By leveraging artificial intelligence, this registry seeks to construct and refine algorithms that able to predict molecular pathology or clinical outcomes of glioma patients based on MR images and histopathology images, as well as revealing related mechanisms from genetic perspective.

Detailed Description

Non-invasive and precise prediction for molecular biomarkers such as 1p/19q co-deletion, MGMT methylation, IDH and TERTp mutations, and patients survival is challenging for gliomas. 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), and in the histopathology images of HE slices of gliomas could be excavated to aid prediction of molecular pathology and patients' survival of gliomas. This study aims to collect clinical, radiological, pathological, molecular and genetic data including detailed clinical parameters, MR and histopathology images, molecular pathology (1p/19q co-deletion, MGMT methylation, IDH and TERTp mutations, etc) and genetic data (Whole exome sequencing, RNA sequencing, proteomics, etc), and seeks to construct and refine algorithms that able to predict molecular pathology or clinical outcomes of glioma patients based on MR images and histopathology images, as well as revealing related mechanisms from genetic perspective.

Design Module

Study Type

Observational

Number of References to an Expanded Access Study

Not provided

Expanded Access Types

Not provided

Patient Registry

Yes

Target Follow-Up Duration

24 Months

Phases

Not provided

Interventional Study Design

Allocation

Not provided

Intervention Model

Biospecimen

Retention

Enrollment

500Estimated

Arms/Interventions Module

Arm Groups

Not provided

Interventions

Name	Type	Description	Arm Group Labels	Other Names
MR and Histopathology images based prediction of molecular pathology and patient survival	Diagnostic Test	MR and Histopathology images based prediction of molecular pathology and patient survival in gliomas by leverage artificial intelligence algorithms

Outcomes Module

Primary Outcomes

Measure	Description	Time Frame
AUC of Prediction performance	AUC of Prediction performance=sensitivity+specificity-1	up to 2 years

Secondary Outcomes

Not provided

Other Outcomes

Not provided

Eligibility Module

Eligibility Criteria

Inclusion Criteria:

Patients must have radiologically and histologically confirmed diagnosis of primary glioma
Life expectancy of greater than 3 months
Must receive tumor resection
Must have sufficient frozen tissues and peripheral blood samples for sequencing
Must have high-quality MR images and histopathology images
Signed informed consent

Exclusion Criteria:

No gliomas
No sufficient amount of tumor tissues for detection of molecular pathology
Patients who are pregnant or breast feeding
Patients who are suffered from severe systematic malfunctions

Accepts Healthy Volunteers

Yes

Sex

All

Sex/Gender Based

Not provided

Sex/Gender Description

Not provided

Minimum Age

1 Year

Maximum Age

95 Years

Standard Ages

ChildAdultOlder Adult

Study Population

Patients with newly diagnosed glioma that receive tumor resection with preoperative MR images and postoperative histopathology images

Sampling Method

Probability Sample

Contacts/Locations Module

Central Contacts

Name	Role	Phone	Extension	Email
Zhenyu Zhang	Contact	+86 17839973727		fcczhangzy1@zzu.edu.cn

Overall Officials

Not provided

Locations

Facility	Status	City	State	ZIP	Country	Contacts
Department of Neurosurgery, First Affiliated Hospital of Zhengzhou University	Recruiting	Zhengzhou	Henan	450052	China

IPD Sharing Statement Module

Plan to Share IPD

Undecided

Description

Undecided

Types

Not provided

Time Frame

Not provided

Access Criteria

Not provided

URL

Not provided

Condition Browse Module

MeSH Terms

ID	Term
D005910	Glioma

Ancestor Terms

ID	Term
D018302	Neoplasms, Neuroepithelial
D017599	Neuroectodermal Tumors
D009373	Neoplasms, Germ Cell and Embryonal
D009370	Neoplasms by Histologic Type

Browse Leaves

Not provided

Browse Branches

Not provided

Description

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,etc are planed to be conducted.