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NCT Number: NCT04217044

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

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.

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Key information

Conditions

Glioma Neoplasms Neoplasms by Histologic Type Neoplasms, Germ Cell and Embryonal Neoplasms, Glandular and Epithelial Neoplasms, Nerve Tissue Neoplasms, Neuroepithelial Neuroectodermal Tumors

Age range

1 year–95 year

Sex eligibility

All sexes

Study type

Observational

Primary location

Department of Neurosurgery, First Affiliated Hospital of Zhengzhou University

Zhengzhou, Henan, 450052, China

Location status: Recruiting

Location contact

Zhenyu Zhang, Dr.

CONTACT

[email protected]

+86 17839973727

About this study

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.

Who can participate

Healthy volunteers accepted: Yes

Only the study team can determine whether someone qualifies for participation.

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

Treatment and study plan

Histopathology images based prediction of molecular pathology

Diagnostic Test

Histopathology images based prediction of 1p/19q co-deletion, MGMT methylation, IDH and TERTp mutations or molecular subgroups by leveraging AI

Primary outcomes

  1. AUC of prediction performance

    Time frame: up to 10 years

    AUC=sensitivity+specificity-1

Study contacts

Contact information is provided by the study sponsor or research team.

Zhenyu Zhang, Dr.

CONTACT

[email protected]

+86 17839973727

Sponsors and collaborators

Lead sponsor

The First Affiliated Hospital of Zhengzhou University

Other

Collaborators

  • Sun Yat-sen University

Registry information

Official study title

Histopathology Images Based Prediction of Molecular Pathology in Glioma Using Deep Learning or Machine Learning

Important dates

Study start
2017
Primary completion
2027
Study completion
2027
First posted
Jan 3, 2020
Registry last updated
Feb 8, 2021

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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