GliomaNCT04215211
MR Based Survival Prediction of Glioma Patients Using Artificial Intelligence
An observational study of Glioma, run by The First Affiliated Hospital of Zhengzhou University at 1 site in China.
Trial summary
This registry aims to collect clinical, molecular and radiologic data including detailed survival data, clinical parameters, molecular pathology (1p/19q codeletion, MGMT methylation, IDH and TERTp mutations, etc) and conventional/advanced/new MR sequences (T1, T1c, T2, FLAIR, ADC, DTI, PWI, etc) of patients with… Read more about this study
- Who is it for?
- Ages 1 year to 90 years
- Conditions the study is for
- Glioma
- What is being tested?
- Survival prediction for glioma patients
- How many people
- 2,500 participants
- Where
- 1 siteChina
- Study dates
- 1 Jan 2017to 1 Jun 2027
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
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 90 years
- All sexes
- No healthy volunteers
- Child, Adult, Older adult
Study population: Paitents with newly diagnosed glioma who receive tumor resection with life expectancy of greater than 3 months
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
- Signed informed consent
Who cannot join
- 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 malfuctions
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
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: MR Based Survival Prediction of Patients With Primary Glioma Ssing Deep Learning or Machine Learning
This registry aims to collect clinical, molecular and radiologic data including detailed survival data, clinical parameters, molecular pathology (1p/19q codeletion, 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 patients' survivals in the frame of molecular pathology or subgroups of gliomas.
Read the 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 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 patients' prognosis in the frame of molecular pathology of gliomas. The creation of a registry for primary glioma with detailed survival data, molecular pathology, radiological 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.
- Conditions
- Glioma
- Being tested
- Survival prediction for glioma patients
- Keywords
- Prognosis, Radiomics, Deep Learning, Machine Learning
- Medical subject headings
- Glioma
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Survival prediction for glioma patientsDiagnostic test
Survival prediction of glioma patients in the frame of molecular pathology by leveraging AI
What is being measured
The primary outcome is the main question the study is designed to answer.
AUC of survival prediction performance
AUC of survival prediction performance=sensitivity+specificity-1
Measured: up to 10 years
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted2 Jan 2020
- Study start1 Jan 2017
- Primary completion1 Jan 2027 (estimated)
- Study completion1 Jun 2027 (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
- Retrospective
- Enrolment
- 2,500 participants (estimated)
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
- NCT04215211
- Sponsor's protocol ID
- GliomaAI-2
- Status last verified
- Feb 2021
- First submitted
- 30 Dec 2019
- Last updated
- 8 Feb 2021
- Results posted
- No
- Data monitoring committee
- No
- 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
- MR Based Survival Prediction of Patients With Primary Glioma Ssing Deep Learning or Machine Learning
- ClinicalTrials.gov ID
- NCT04215211
- Acronym / sponsor ID
- GliomaAI-2
- Conditions
- Glioma
- Keywords
- Prognosis, Radiomics, Deep Learning, Machine Learning
- Study type
- Observational
- Recruitment status
- Recruiting
- Enrolment
- 2,500 participants (estimated)
- Lead sponsor
- The First Affiliated Hospital of Zhengzhou UniversityUniversity, hospital or other
- Collaborators
- Sun Yat-sen University
- Study start
- 1 Jan 2017 (actual)
- Primary completion
- 1 Jan 2027 (estimated)
- Study completion
- 1 Jun 2027 (estimated)
- First posted
- 2 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 registry aims to collect clinical, molecular and radiologic data including detailed survival data, clinical parameters, molecular pathology (1p/19q codeletion, 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 patients' survivals in the frame of molecular pathology or subgroups of gliomas.
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 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 patients' prognosis in the frame of molecular pathology of gliomas. The creation of a registry for primary glioma with detailed survival data, molecular pathology, radiological 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 design
- Observational model
- Cohort
- Time perspective
- Retrospective
Who may qualify
- Ages
- 1 year to 90 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Child, Adult, Older adult
- Study population
- Paitents with newly diagnosed glioma who receive tumor resection with life expectancy of greater than 3 months
Key inclusion criteria (4 listed)
- 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
Key exclusion criteria (5 listed)
- 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 malfuctions
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 |
|---|---|---|
| Survival prediction for glioma patientsSurvival prediction of glioma patients in the frame of molecular pathology by leveraging AI | Diagnostic test | – |
Primary outcome
AUC of survival prediction performance
Time frame: up to 10 years
AUC of survival prediction performance=sensitivity+specificity-1
Results and safety
This study is still under way. Results are not available in the source record yet.
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.
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 4 inclusion and 5 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: looking for participants now, 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?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Zhenyu Zhang, Dr. (+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 survival prediction performance (time frame: up to 10 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.