Clinical trial NCT04220424: Glioma Patients Registry Based on Radiological, Histopathological and Genetic Analysis
Official title: Glioma Patients Registry Based on MR Images, Histopathology Images and Genetic Sequencing Analyzed by Artificial Intelligence
Study type: OBSERVATIONAL
Overall status: UNKNOWN
Conditions: Glioma
Keywords: Artificial intelligence
MR images
histopathology images
sequencing
mechanism
Lead sponsor: The First Affiliated Hospital of Zhengzhou University
Sponsor class: OTHER
Collaborators: name: Sun Yat-sen University
First posted: 2020-01-07
Enrollment: 500
Sex: ALL
Minimum age (years): 1
Maximum age (years): 95
Standard ages: CHILD
ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Observational model: COHORT
Time perspective: PROSPECTIVE
Sampling method: PROBABILITY_SAMPLE
Study population: Patients with newly diagnosed glioma that receive tumor resection with preoperative MR images and postoperative histopathology images

Brief summary: This prospective study aims to collect clinical, radiological, pathological, molecular and genetic data including detailed clinical parameters, MR and histopathology images, molecular pathology and genetic sequencing data. By leveraging artificial intelligence, this registry seeks to construct and refine algorithms that able to predict molecular pathology or clinical outcomes of glioma patients based on MR images and histopathology images, as well as revealing related mechanisms from genetic perspective.

Detailed description: Non-invasive and precise prediction for molecular biomarkers such as 1p/19q co-deletion, MGMT methylation, IDH and TERTp mutations, and patients survival is challenging for gliomas. 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), and in the histopathology images of HE slices of gliomas could be excavated to aid prediction of molecular pathology and patients' survival of gliomas. This study aims to collect clinical, radiological, pathological, molecular and genetic data including detailed clinical parameters, MR and histopathology images, molecular pathology (1p/19q co-deletion, MGMT methylation, IDH and TERTp mutations, etc) and genetic data (Whole exome sequencing, RNA sequencing, proteomics, etc), and seeks to construct and refine algorithms that able to predict molecular pathology or clinical outcomes of glioma patients based on MR images and histopathology images, as well as revealing related mechanisms from genetic perspective.

Eligibility 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
* Must have sufficient frozen tissues and peripheral blood samples for sequencing
* Must have high-quality MR images and histopathology images
* 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

Primary outcomes:
measure: AUC of Prediction performance
timeFrame: up to 2 years
Secondary outcomes:
Other outcomes:
Locations:
city: Zhengzhou
country: China
facility: Department of Neurosurgery, First Affiliated Hospital of Zhengzhou University
state: Henan
Study officials:
References: