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Histopathology Images Based Prediction of Molecular Pathology in Glioma Using Artificial Intelligence

The safety and scientific validity of this study is the responsibility of the study sponsor and investigators. Listing a study does not mean it has been evaluated by the U.S. Federal Government. Know the risks and potential benefits of clinical studies and talk to your health care provider before participating. Read our disclaimer for details.
 
ClinicalTrials.gov Identifier: NCT04217044
Recruitment Status : Recruiting
First Posted : January 3, 2020
Last Update Posted : January 6, 2020
Sponsor:
Collaborator:
Sun Yat-sen University
Information provided by (Responsible Party):
Zhenyu Zhang, The First Affiliated Hospital of Zhengzhou University

Brief Summary:
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 images of HE slices in primary gliomas. By leveraging artificial intelligence, this registry will seek to construct and refine histopathology image based algorithms that are able to predict molecular pathology or subgroups of gliomas.

Condition or disease Intervention/treatment
Glioma Diagnostic Test: Histopathology images based prediction of molecular pathology

Detailed Description:
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 histopathology images of HE slices in primary gliomas could be excavated to aid prediction of molecular pathology of gliomas. The creation of a registry for primary glioma with detailed molecular pathology, histopathology image 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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Study Type : Observational [Patient Registry]
Estimated Enrollment : 3000 participants
Observational Model: Cohort
Time Perspective: Other
Target Follow-Up Duration: 120 Months
Official Title: Histopathology Images Based Prediction of Molecular Pathology in Glioma Using Deep Learning or Machine Learning
Actual Study Start Date : January 1, 2017
Estimated Primary Completion Date : January 1, 2027
Estimated Study Completion Date : June 1, 2027

Resource links provided by the National Library of Medicine



Intervention Details:
  • Diagnostic Test: Histopathology images based prediction of molecular pathology
    Histopathology images based prediction of 1p/19q co-deletion, MGMT methylation, IDH and TERTp mutations or molecular subgroups by leveraging AI


Primary Outcome Measures :
  1. AUC of prediction performance [ Time Frame: up to 10 years ]
    AUC=sensitivity+specificity-1


Biospecimen Retention:   Samples With DNA
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.


Information from the National Library of Medicine

Choosing to participate in a study is an important personal decision. Talk with your doctor and family members or friends about deciding to join a study. To learn more about this study, you or your doctor may contact the study research staff using the contacts provided below. For general information, Learn About Clinical Studies.


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Ages Eligible for Study:   1 Year to 95 Years   (Child, Adult, Older Adult)
Sexes Eligible for Study:   All
Accepts Healthy Volunteers:   Yes
Sampling Method:   Probability Sample
Study Population
Patients with newly diagnosed glioma that receive tumor resection
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
  • 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

Information from the National Library of Medicine

To learn more about this study, you or your doctor may contact the study research staff using the contact information provided by the sponsor.

Please refer to this study by its ClinicalTrials.gov identifier (NCT number): NCT04217044


Contacts
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Contact: Zhenyu Zhang, Dr. +86 17839973727 fcczhangzy1@zzu.edu.cn

Locations
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China, Henan
Department of Neurosurgery, First Affiliated Hospital of Zhengzhou University Recruiting
Zhengzhou, Henan, China, 450052
Contact: Zhenyu Zhang, Dr.    +86 17839973727    fcczhangzy1@zzu.edu.cn   
Sponsors and Collaborators
The First Affiliated Hospital of Zhengzhou University
Sun Yat-sen University
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Responsible Party: Zhenyu Zhang, Principal Investigator, The First Affiliated Hospital of Zhengzhou University
ClinicalTrials.gov Identifier: NCT04217044    
Other Study ID Numbers: GliomaAI-3
First Posted: January 3, 2020    Key Record Dates
Last Update Posted: January 6, 2020
Last Verified: January 2020
Individual Participant Data (IPD) Sharing Statement:
Plan to Share IPD: Undecided
Plan Description: Undecided

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Studies a U.S. FDA-regulated Drug Product: No
Studies a U.S. FDA-regulated Device Product: No
Keywords provided by Zhenyu Zhang, The First Affiliated Hospital of Zhengzhou University:
Molecular
Histopathology image
Deep Learning
Machine Learning
Additional relevant MeSH terms:
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Glioma
Neoplasms, Neuroepithelial
Neuroectodermal Tumors
Neoplasms, Germ Cell and Embryonal
Neoplasms by Histologic Type
Neoplasms
Neoplasms, Glandular and Epithelial
Neoplasms, Nerve Tissue