MR Based Prediction of Molecular Pathology in Glioma Using Artificial Intelligence

MR Based Prediction of Molecular Biomarkers or Subgroups in Primary Glioma Using Deep Learning or Machine Learning

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 conventional/advanced/new MR sequences (T1, T1c, T2, FLAIR, ADC, DTI, PWI, etc) of patients with primary gliomas. By leveraging artificial intelligence, this registry will seek to construct and refine algorithms that able to predict molecular pathology or subgroups of gliomas.

Study Overview

• Status: Recruiting
• Conditions: Glioma
• Intervention / Treatment: Diagnostic test: 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 preoperative conventional/advanced MR imaging (T1 weighted imaging, T2 weighted imaging, FLAIR, contrast-enhanced T1 weighted imaging, diffusion-weighted imaging, and perfusion imaging) could be excavated to aid prediction of molecular pathology of gliomas. The creation of a registry for primary glioma with detailed molecular pathology, radiological data and with sufficient sample size for deep learning (>1000) provide considerable opportunities for personalized prediction of molecular pathology with non-invasiveness and precision.

• Study Type: Observational
• Enrollment (Anticipated): 3000

Contacts and Locations

• Study Contact: Name: Zhenyu Zhang, Dr.; Phone Number: +86 17839973727; Email: fcczhangzy1@zzu.edu.cn

Study Locations

• China
  • Henan
    • Zhengzhou, Henan, China, 450052
      • Recruiting
      • Department of Neurosurgery, First Affiliated Hospital of Zhengzhou University
      • Contact: Zhenyu Zhang, Dr.; Phone Number: +86 17839973727; Email: fcczhangzy1@zzu.edu.cn

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: 1 year to 95 years (Child, Adult, Older Adult)
• Accepts Healthy Volunteers: Yes
• Genders Eligible for Study: All

• Sampling Method: Probability Sample

Study Population

Patients with newly diagnosed glioma that receive tumor resection

Description

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 have any type of bioimplant activated by mechanical, electronic, or magnetic devices
• Patients who are pregnant or breast feeding
• Patients who are suffered from severe systematic malfunctions

Study Plan

How is the study designed?

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
AUC of prediction performance	AUC=sensitivity+specificity-1	up to 10 years

Collaborators and Investigators

• Sponsor: The First Affiliated Hospital of Zhengzhou University
• Collaborators: Sun Yat-sen University

Study record dates

Study Major Dates

• Study Start (Actual): January 1, 2017
• Primary Completion (Anticipated): January 1, 2027
• Study Completion (Anticipated): June 1, 2027

Study Registration Dates

• First Submitted: December 30, 2019
• First Submitted That Met QC Criteria: December 31, 2019
• First Posted (Actual): January 3, 2020

Study Record Updates

• Last Update Posted (Actual): February 8, 2021
• Last Update Submitted That Met QC Criteria: February 5, 2021
• Last Verified: February 1, 2021

More Information

Terms related to this study

• Keywords: machine learning; deep learning; radiomics; molecular
• Additional Relevant MeSH Terms: Neoplasms by Histologic Type; Neoplasms; Neoplasms, Glandular and Epithelial; Neoplasms, Neuroepithelial; Neuroectodermal Tumors; Neoplasms, Germ Cell and Embryonal; Neoplasms, Nerve Tissue; Glioma
• Other Study ID Numbers: GliomaAI-1

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: UNDECIDED
• IPD Plan Description: Undecided.

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No