Histopathology Images Based Survival Prediction of Glioma Patients Using Artificial Intelligence

Histopathology Images Based Survival Prediction of Patients With Primary Glioma Using Deep Learning or Machine Learning

This registry aims to collect clinical, molecular and histopathology imaging including detailed survival data, clinical parameters, molecular pathology (1p/19q codeletion, 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 hstopathology imaging based algorithms that able to predict patients' survivals in the frame of molecular pathology or subgroups of gliomas.

Study Overview

• Status: Recruiting
• Conditions: Glioma
• Intervention / Treatment: Diagnostic test: Histopathology images based survival prediction for glioma patients

Detailed Description

Non-invasive and precise prediction for survivals of glioma patients 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 patients' prognosis in the frame of molecular pathology of gliomas. The creation of a registry for primary glioma with detailed survival data, molecular pathology, histopathology image data and with sufficient sample size for deep learning (>1000) provides opportunities for personalized prediction of survival of glioma patients with non-invasiveness and precision.

• Study Type: Observational
• Enrollment (Anticipated): 3500

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 90 years (Child, Adult, Older Adult)
• Accepts Healthy Volunteers: No
• Genders Eligible for Study: All

• Sampling Method: Probability Sample

Study Population

Paitents with newly diagnosed glioma who receive tumor resection with life expectancy of greater than 3 months

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 are pregnant or breast feeding
• Patients who are suffered from severe systematic malfuctions

Study Plan

How is the study designed?

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
AUC of survival prediction performance	AUC of survival prediction performance=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 30, 2019
• First Posted (Actual): January 2, 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: Prognosis; Deep Learning; Machine Learning; Histopathology imaging
• 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-4

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