High-grade GliomaNCT04908267
How the Precise Habitats Can Predict the IDH Mutation Status and Prognosis of the Patients With High-grade Gliomas
An observational study of High-grade Glioma, run by Weiguo Zhang at 1 site in China.
Trial summary
High-grade glioma is the most common primary malignant tumor in central nervous system, and its high tumor heterogeneity is the main cause of tumor progression, treatment resistance and recurrence. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- High-grade Glioma
- How many people
- 100 participants
- Where
- 1 siteChina
- Study dates
- 1 Aug 2022to 31 Dec 2024
- 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 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: The subjects we selected are adults who are not restricted by gender. For details, please refer to the "criteria" column.
Who may be able to join
None listed.
Who cannot join
- patient with other brain tumors or other grade gliomas at the same time;
- patient with severe basic diseases at the same time;
- patient with a survival time of less than 30 days, which can be caused by severe surgical trauma stress;
- poor image quality and heavy artifact affect the subsequent image processing.
Trial locations and contacts
1 site in 1 country.
China1 site, 1 recruiting
- Recruiting
Department of Radiology, Daping Hospital of Army Medical University
Chongqing, Chongqing Municipality, 400042
Jiachen Liu, master, jcliu0430@163.com
About this study
Official title: To Determine Whether the Habitats Created by the Preoperative or Postoperative Multiparametric MRI Can Predict IDH Mutation Status, Prognosis, Recurrence or Intracranial Distant Recurrence of the Patients With High-grade Gliomas
High-grade glioma is the most common primary malignant tumor in central nervous system, and its high tumor heterogeneity is the main cause of tumor progression, treatment resistance and recurrence. Habitat imaging is a segmentation technique by dividing tumor regions to characterize tumor heterogeneity based on tumor pathology, blood perfusion, molecular characteristics and other tumor biological features.
In some studies, the Hemodynamic Multiparametric Tissue Signature (HTS) method has been proven to be feasible. The Hemodynamic Multiparametric Tissue Signature (HTS) consists of a set of vascular habitats obtained by Dynamic Susceptibility Weighted Contrast Enhanced Magnetic Resonance Imaging (DSC-MRI) of high-grade gliomas using a multiparametric unsupervised analysis method. This allowed them to automatically draw 4 reproducible vascular habitats (High-angiogenic enhancing tumor; Low-angiogenic enhancing tumor; Potentially tumor infiltrated peripheral edema; Vasogenic peripheral edema) which enable to describe the tumor vascular heterogeneity robustly.
Read more
In other studies, contrast-enhancing mass can divided into spatial habitats by K-means clustering of voxel-wise apparent diffusion coefficient (ADC) and cerebral blood volume (CBV) values to observe the changes of voxels in spatial habitat on the time line. Using this so-called spatiotemporal habitat to identify progression or pseudoprogression in cancer therapy.
Above all, we have sufficient and firm reasons to deem that habitat imaging based on multiparametric MRI is more conducive to reflect the potential biological information inside the tumor and realize individualized diagnosis and treatment.
To sum up, the assumption of this experiment is that the Habitats Created by preoperative or postoperative Multiparametric MRI ,such as conventional MRI sequences, Dynamic Susceptibility Weighted Contrast Enhanced Magnetic Resonance Imaging (DSC-MRI), Dynamic Contrast Enhanced Magnetic Resonance Imaging (DCE-MRI), Diffusion Weighted Magnetic Resonance Imaging(DWI) ,Vessel Size Imaging (VSI) ,or Magnetic Resonance Spectroscopy (MRS) can predict the molecular mutation status, prognosis, treatment residence, progression, pseudoprogression, and even recurrence and distant intracranial recurrence in patients with high-grade gliomas.
Read the detailed description
This is a single center experiment. The subjects of this study were patients diagnosed as high-grade glioma by multiparametric magnetic resonance imaging and pathological biopsy from January 1, 2008 to December 31, 2021(or at some interval within this period). Patients meeting the inclusion criteria will enter the next experimental stage.
- Patients selection: The patients will be clustered according to the preoperative and postoperative examination methods performed by each patient, such as only DSC sequence, both DSC and DWI sequences, or a full set of functional imaging sequences at the same time;
- Images segmentation: To select the patients who meet the experimental design, and use deep learning-based biomedical image segmentation methods, such as Brain Tumor Segmentation (BraTS) challenge, to segment more accurate and reproducible habitats as much as possible;
- Construction of clinical model: We may be able to obtain the parameter values of the habitat, such as CBV, ADC, Ktrans, etc. Combining these data with the basic situation of patients can build a clinical model.
- Construction of radiomics model: The radiomics analysis will probably be structured into four parts: habitats segmentation, feature extraction, feature selection and model construction.
- We try to analyze that habitat imaging based on Multiparametric MRI is indeed better than the conventional rough and simple ROI analysis; We try to analyze the relation between the habitats and the IDH mutation status or MGMT promoter methylation status; We try to analyze the relation between the habitats and the overall survival (OS) of the patient; We try to analyze the habitats is conducive to differentiate recurrence from distant intracranial recurrence.
Finally, statistical methods and survival analysis were used to determine whether the habitat was statistically significant for IDH mutation status and prognosis. For example, receiver operating characteristic curve (ROC) analysis evaluated the potential of the spatial habitats in IDH mutation prediction. The Kaplan-Meier curve evaluates the validation of the diagnosis in OS prediction in high-grade glioma.
- Conditions
- High-grade Glioma
- Keywords
- habitat imaging, high-grade glioma, radiomics, heterogeneity, recurrence
- Medical subject headings
- Glioma, Recurrence
What is being measured
The primary outcome is the main question the study is designed to answer.
Multi-model habitats constructed by multiparametric MRI predict IDH mutation status and the prognosis in high-grade gliomas
The IDH status of each patient was dependent on pathological and immunohistochemical results. The overall survival for each patient is estimated since the date of operation to the end of recruitment. The overall survival will be confirmed through clinical follow-up.
Measured: From the date of operation until the date of death from any cause，assessed up to 120 months
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted1 Jun 2021
- Study start1 Aug 2022
- Primary completion31 Dec 2024 (estimated)
- Study completion31 Dec 2024 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (1)Papers the sponsor has linked to this study.
- Other trials for High-grade GliomaStudies of the same condition, open and under way.
- 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
- Case only
- Time perspective
- Retrospective
- Enrolment
- 100 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Weiguo Zhang
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor-investigator, Weiguo Zhang, Daping Hospital and the Research Institute of Surgery of the Third Military Medical University
- Investigators
- Weiguo Zhang, Ph.D, Principal investigator, Daping Hospital and the Research Institute of Surgery of the Third Military Medical University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT04908267
- Sponsor's protocol ID
- 2022198
- Status last verified
- Jan 2023
- First submitted
- 17 May 2021
- Last updated
- 11 Jan 2023
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Publications
Wu H, Tong H, Du X, Guo H, Ma Q, Zhang Y, Zhou X, Liu H, Wang S, Fang J, Zhang W. Vascular habitat analysis based on dynamic susceptibility contrast perfusion MRI predicts IDH mutation status and prognosis in high-grade gliomas. Eur Radiol. 2020 Jun;30(6):3254-3265. doi: 10.1007/s00330-020-06702-2. Epub 2020 Feb 20.
PubMed 32078014
Study at a glance
From the registry record, last updated by the sponsor on 11 Jan 2023. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- To Determine Whether the Habitats Created by the Preoperative or Postoperative Multiparametric MRI Can Predict IDH Mutation Status, Prognosis, Recurrence or Intracranial Distant Recurrence of the Patients With High-grade Gliomas
- ClinicalTrials.gov ID
- NCT04908267
- Acronym / sponsor ID
- 2022198
- Conditions
- High-grade Glioma
- Keywords
- habitat imaging, high-grade glioma, radiomics, heterogeneity, recurrence
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 100 participants (estimated)
- Lead sponsor
- Weiguo ZhangUniversity, hospital or other
- Study start
- 1 Aug 2022 (actual)
- Primary completion
- 31 Dec 2024 (estimated)
- Study completion
- 31 Dec 2024 (estimated)
- First posted
- 1 Jun 2021 (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.
High-grade glioma is the most common primary malignant tumor in central nervous system, and its high tumor heterogeneity is the main cause of tumor progression, treatment resistance and recurrence. Habitat imaging is a segmentation technique by dividing tumor regions to characterize tumor heterogeneity based on tumor pathology, blood perfusion, molecular characteristics and other tumor biological features.
In some studies, the Hemodynamic Multiparametric Tissue Signature (HTS) method has been proven to be feasible. The Hemodynamic Multiparametric Tissue Signature (HTS) consists of a set of vascular habitats obtained by Dynamic Susceptibility Weighted Contrast Enhanced Magnetic Resonance Imaging (DSC-MRI) of high-grade gliomas using a multiparametric unsupervised analysis method. This allowed them to automatically draw 4 reproducible vascular habitats (High-angiogenic enhancing tumor; Low-angiogenic enhancing tumor; Potentially tumor infiltrated peripheral edema; Vasogenic peripheral edema) which enable to describe the tumor vascular heterogeneity robustly.
In other studies, contrast-enhancing mass can divided into spatial habitats by K-means clustering of voxel-wise apparent diffusion coefficient (ADC) and cerebral blood volume (CBV) values to observe the changes of voxels in spatial habitat on the time line. Using this so-called spatiotemporal habitat to identify progression or pseudoprogression in cancer therapy.
Above all, we have sufficient and firm reasons to deem that habitat imaging based on multiparametric MRI is more conducive to reflect the potential biological information inside the tumor and realize individualized diagnosis and treatment.
To sum up, the assumption of this experiment is that the Habitats Created by preoperative or postoperative Multiparametric MRI ,such as conventional MRI sequences, Dynamic Susceptibility Weighted Contrast Enhanced Magnetic Resonance Imaging (DSC-MRI), Dynamic Contrast Enhanced Magnetic Resonance Imaging (DCE-MRI), Diffusion Weighted Magnetic Resonance Imaging(DWI) ,Vessel Size Imaging (VSI) ,or Magnetic Resonance Spectroscopy (MRS) can predict the molecular mutation status, prognosis, treatment residence, progression, pseudoprogression, and even recurrence and distant intracranial recurrence in patients with high-grade gliomas.
Detailed description
This is a single center experiment. The subjects of this study were patients diagnosed as high-grade glioma by multiparametric magnetic resonance imaging and pathological biopsy from January 1, 2008 to December 31, 2021(or at some interval within this period). Patients meeting the inclusion criteria will enter the next experimental stage.
- Patients selection: The patients will be clustered according to the preoperative and postoperative examination methods performed by each patient, such as only DSC sequence, both DSC and DWI sequences, or a full set of functional imaging sequences at the same time;
- Images segmentation: To select the patients who meet the experimental design, and use deep learning-based biomedical image segmentation methods, such as Brain Tumor Segmentation (BraTS) challenge, to segment more accurate and reproducible habitats as much as possible;
- Construction of clinical model: We may be able to obtain the parameter values of the habitat, such as CBV, ADC, Ktrans, etc. Combining these data with the basic situation of patients can build a clinical model.
- Construction of radiomics model: The radiomics analysis will probably be structured into four parts: habitats segmentation, feature extraction, feature selection and model construction.
- We try to analyze that habitat imaging based on Multiparametric MRI is indeed better than the conventional rough and simple ROI analysis; We try to analyze the relation between the habitats and the IDH mutation status or MGMT promoter methylation status; We try to analyze the relation between the habitats and the overall survival (OS) of the patient; We try to analyze the habitats is conducive to differentiate recurrence from distant intracranial recurrence.
Finally, statistical methods and survival analysis were used to determine whether the habitat was statistically significant for IDH mutation status and prognosis. For example, receiver operating characteristic curve (ROC) analysis evaluated the potential of the spatial habitats in IDH mutation prediction. The Kaplan-Meier curve evaluates the validation of the diagnosis in OS prediction in high-grade glioma.
Study design
- Observational model
- Case only
- Time perspective
- Retrospective
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- The subjects we selected are adults who are not restricted by gender. For details, please refer to the "criteria" column.
Key inclusion criteria (0 listed)
Key exclusion criteria (4 listed)
- patient with other brain tumors or other grade gliomas at the same time;
- patient with severe basic diseases at the same time;
- patient with a survival time of less than 30 days, which can be caused by severe surgical trauma stress;
- poor image quality and heavy artifact affect the subsequent image processing.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
Multi-model habitats constructed by multiparametric MRI predict IDH mutation status and the prognosis in high-grade gliomas
Time frame: From the date of operation until the date of death from any cause，assessed up to 120 months
The IDH status of each patient was dependent on pathological and immunohistochemical results. The overall survival for each patient is estimated since the date of operation to the end of recruitment. The overall survival will be confirmed through clinical follow-up.
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 11 Jan 2023.
- Background: PubMed 32078014Wu H, Tong H, Du X, Guo H, Ma Q, Zhang Y, Zhou X, Liu H, Wang S, Fang J, Zhang W. Vascular habitat analysis based on dynamic susceptibility contrast perfusion MRI predicts IDH mutation status and prognosis in high-grade gliomas. Eur Radiol. 2020 Jun;30(6):3254-3265. doi: 10.1007/s00330-020-06702-2. Epub 2020 Feb 20.
- Vascular habitat analysis based on dynamic susceptibility contrast perfusion MRI predicts IDH mutation status and prognosis in high-grade gliomasLink provided by the sponsor.
- Individual participant dataSharing planned: No
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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.
Weiguo Zhang, Ph.D principal investigator
Daping Hospital and the Research Institute of Surgery of the Third Military Medical University
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 High-grade Glioma, run by Weiguo Zhang 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 0 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 11 Jan 2023. 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 Jiachen Liu, M.D. ((+86)18434161824, JCliu0430@163.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Multi-model habitats constructed by multiparametric MRI predict IDH mutation status and the prognosis in high-grade gliomas (time frame: From the date of operation until the date of death from any cause，assessed up to 120 months). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT04908267, which also lists 1 linked publication.
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 11 Jan 2023.