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

Zhengzhou University

Status: Enrolling

Conditions: Glioma

Treatments: Diagnostic Test: Histopathology images based prediction of molecular pathology

Study type: Observational

Funder types: Other

Identifiers

NCT04217044

GliomaAI-3

Details and patient eligibility

About

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.

Full 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.

Enrollment: 3,000 estimated patients

Sex: All

Ages: 1 to 95 years old

Volunteers: Accepts Healthy Volunteers

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