GliomaNCT04220424
Glioma Patients Registry Based on Radiological, Histopathological and Genetic Analysis
An observational study of Glioma, run by The First Affiliated Hospital of Zhengzhou University at 1 site in China.
Trial 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. Read more about this study
- Who is it for?
- Ages 1 year to 95 yearsHealthy volunteers can take part
- Conditions the study is for
- Glioma
- What is being tested?
- MR and Histopathology images based prediction of molecular pathology and patient survival
- How many people
- 500 participants
- Where
- 1 siteChina
- Study dates
- 1 Nov 2018to 1 Mar 2022
- How long will I take part?
- 24 Months
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 1 year to 95 years
- All sexes
- Healthy volunteers accepted
- Child, Adult, Older adult
Study population: Patients with newly diagnosed glioma that receive tumor resection with preoperative MR images and postoperative histopathology images
Who may be able to join
- 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
Who cannot join
- 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
Trial locations and contacts
1 site in 1 country.
China1 site, 1 recruiting
- Recruiting
Department of Neurosurgery, First Affiliated Hospital of Zhengzhou University
Zhengzhou, Henan, 450052
Zhenyu Zhang, Dr., +86 17839973727, fcczhangzy1@zzu.edu.cn
About this study
Official title: Glioma Patients Registry Based on MR Images, Histopathology Images and Genetic Sequencing Analyzed by Artificial Intelligence
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.
Read the 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.
- Conditions
- Glioma
- Being tested
- MR and Histopathology images based prediction of molecular pathology and patient survival
- Keywords
- Artificial intelligence, MR images, histopathology images, sequencing, mechanism
- Medical subject headings
- Glioma
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
MR and Histopathology images based prediction of molecular pathology and patient survivalDiagnostic test
MR and Histopathology images based prediction of molecular pathology and patient survival in gliomas by leverage artificial intelligence algorithms
What is being measured
The primary outcome is the main question the study is designed to answer.
AUC of Prediction performance
AUC of Prediction performance=sensitivity+specificity-1
Measured: up to 2 years
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted7 Jan 2020
- Study start1 Nov 2018
- Primary completion1 Jan 2022 (estimated)
- Study completion1 Mar 2022 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for GliomaStudies of the same condition, open and under way.
- More trials run by The First Affiliated Hospital of Zhengzhou UniversityThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Prospective
- Enrolment
- 500 participants (estimated)
- Target duration
- 24 Months
Sponsor and investigators
- Sponsor type
- University, hospital or other
- Collaborators
- Sun Yat-sen University
- Responsible party
- Principal investigator, Zhenyu Zhang, The First Affiliated Hospital of Zhengzhou University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT04220424
- Sponsor's protocol ID
- GliomaAI-6
- Status last verified
- Feb 2021
- First submitted
- 4 Jan 2020
- Last updated
- 8 Feb 2021
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Undecided: Undecided
Study at a glance
From the registry record, last updated by the sponsor on 8 Feb 2021. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Glioma Patients Registry Based on MR Images, Histopathology Images and Genetic Sequencing Analyzed by Artificial Intelligence
- ClinicalTrials.gov ID
- NCT04220424
- Acronym / sponsor ID
- GliomaAI-6
- Conditions
- Glioma
- Keywords
- Artificial intelligence, MR images, histopathology images, sequencing, mechanism
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 500 participants (estimated)
- Lead sponsor
- The First Affiliated Hospital of Zhengzhou UniversityUniversity, hospital or other
- Collaborators
- Sun Yat-sen University
- Study start
- 1 Nov 2018 (actual)
- Primary completion
- 1 Jan 2022 (estimated)
- Study completion
- 1 Mar 2022 (estimated)
- First posted
- 7 Jan 2020 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Target follow-up
- 24 Months
Who may qualify
- Ages
- 1 year to 95 years
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Child, Adult, Older adult
- Study population
- Patients with newly diagnosed glioma that receive tumor resection with preoperative MR images and postoperative histopathology images
Key inclusion criteria (6 listed)
- 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
Key exclusion criteria (4 listed)
- 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
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| MR and Histopathology images based prediction of molecular pathology and patient survivalMR and Histopathology images based prediction of molecular pathology and patient survival in gliomas by leverage artificial intelligence algorithms | Diagnostic test | – |
Primary outcome
AUC of Prediction performance
Time frame: up to 2 years
AUC of Prediction performance=sensitivity+specificity-1
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 8 Feb 2021.
- Individual participant dataSharing planned: Undecided. Undecided
Related trials for Glioma
Active, not recruitingPhase 1/2
Selumetinib in Treating Young Patients With Recurrent or Refractory Low Grade Glioma
Low Grade Glioma, Recurrent Childhood Pilocytic Astrocytoma
Active, not recruitingPhase 3
EF-41/KEYNOTE D58: Phase 3 Study of Optune Concomitant With Temozolomide Plus Pembrolizumab in Newly Diagnosed Glioblastoma
Glioblastoma
RecruitingPhase 3
A Study of the Drugs Selumetinib vs. Carboplatin and Vincristine in Patients With Low-Grade Glioma
Low Grade Astrocytoma, Low Grade Glioma
RecruitingPhase 3
Beginning Radiation Immediately With GammaTile at GBM Excision Versus Standard of Care
Glioblastoma
RecruitingPhase 1/2
Testing the Addition of an Anti-cancer Drug, Selinexor, to the Usual Chemotherapy Treatment (Temozolomide) for Brain Tumors That Have Returned After Previous Treatment
Recurrent Glioblastoma, IDH-Wildtype, Recurrent MGMT-Methylated Glioblastoma
Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Glioma, run by The First Affiliated Hospital of Zhengzhou University at 1 site in China.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 6 inclusion and 4 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: status not confirmed, as of the sponsor's last update on 8 Feb 2021. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in China. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
Contact Zhenyu Zhang (+86 17839973727, fcczhangzy1@zzu.edu.cn), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
AUC of Prediction performance (time frame: up to 2 years). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 8 Feb 2021.