MeningiomaNCT05093751
Automated Segmentation and Volumetry for Meningioma Using Deep Learning
An observational study of Meningioma and Artificial Intelligence, run by Seoul National University Hospital.
Trial summary
U-Net-based architectures will be applied to 500 contrast-enhanced axial MR images of different patients from a single institution after manual segmentation of meningioma, of which 50 were used for testing. Tumor volumetry after autosegmentation by trained U-Net-based architecture is final goal. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Meningioma, Artificial Intelligence
- What is being tested?
- Observation
- How many people
- 600 participants
- Where
- No sites listed yet
- Study dates
- 23 Mar 2013to 30 Sep 2021
- 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: Intracranial meningioma patients who were diagnosed by MRI are study population of this study. Inclusion in this study have not been decided according to whether or not surgery for tumor resection was performed or MRI thickness and magnetic power.
Who may be able to join
- Radiologically diagnosed meningioma by MRI
Who cannot join
- under 18 years old
- Multiple meningiomas
- Orbital meningioma
- Any prior treatment for intracranial meningioma before registration
About this study
Official title: Automated Meningioma Segmentation and Volumetry Using a nnU-Net Based Architecture on Contrast-enhanced MRI
U-Net-based architectures will be applied to 500 contrast-enhanced axial MR images of different patients from a single institution after manual segmentation of meningioma, of which 50 were used for testing. Tumor volumetry after autosegmentation by trained U-Net-based architecture is final goal.
Read the detailed description
U-Net-based architectures will be applied to 500 contrast-enhanced axial MR images of different patients from a single institution after manual segmentation of meningioma, of which 50 were used for testing. After preprocessing with Z-isotropification and intensity normalization of images, 3 U-Net-based networks (2D U-Net, Attention U-Net, 3D U-Net) and 3 nnU-Net-based networks (2D nnU-Net, Attention nnU-Net, 3D nnU-Net) will be trained with meningioma-segmented images. For applying to 3D networks, sagittal and coronal images will be reconstructed using axial images. After prediction, the cut-off of the probability function, which is a trade-off, will be obtained with the Gaussian Mixture Modeling algorithm using the probability density function. The voxels having a probability function higher than that will be finally predicted as meningioma. Tumor volume is calculated as the sum of the product of segmented area and thickness of axial images. For performance evaluation, dice similarity coefficient (DSC), precision, and recall will be evaluated compared with manually segmented voxels for validation datasets. The results of volumetry of each model will be compared with manual segmentation-based volume through Pearson's correlation analysis.
- Conditions
- Meningioma, Artificial Intelligence
- Being tested
- Observation
- Keywords
- Meningioma, Artificial intelligence, autosegmentation, volumetry
- Medical subject headings
- Meningioma
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
ObservationOther
This study does not involve any intervention to subjects.
| Study group | Role | What they receive |
|---|---|---|
| Meningioma patients | Not described Receives: Other: Observation |
What is being measured
The primary outcome is the main question the study is designed to answer.
Accuracy compared with ground truth
As a primary endpoint, we will examine the ability of U-Net and nnU-Net to segment meningioma in brain MR compared with ground truth. Ground truth is defined as area on MR drawn by two neurosurgeons. Accuracy of autosegmentation of meningioma will be assessed in dice similarity coefficient, recall, and precision.
Measured: 10-01-2020 until 09-30-2021
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted26 Oct 2021
- Study start23 Mar 2013
- Primary completion30 Sep 2021
- Study completion30 Sep 2021
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for MeningiomaStudies of the same condition, open and under way.
- More trials run by Seoul National University HospitalThe 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
- Case only
- Time perspective
- Retrospective
- Enrolment
- 600 participants (actual)
Sponsor and investigators
- Lead sponsor
- Seoul National University Hospital
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Chul-Kee Park, Seoul National University Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05093751
- Sponsor's protocol ID
- SNUH-MNG-AI001
- Status last verified
- Oct 2021
- First submitted
- 1 Oct 2021
- Last updated
- 26 Oct 2021
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No: We have no plan to share IPD
Study at a glance
From the registry record, last updated by the sponsor on 26 Oct 2021. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Automated Meningioma Segmentation and Volumetry Using a nnU-Net Based Architecture on Contrast-enhanced MRI
- ClinicalTrials.gov ID
- NCT05093751
- Acronym / sponsor ID
- SNUH-MNG-AI001
- Conditions
- Meningioma, Artificial Intelligence
- Keywords
- Meningioma, Artificial intelligence, autosegmentation, volumetry
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 600 participants (actual)
- Lead sponsor
- Seoul National University HospitalUniversity, hospital or other
- Study start
- 23 Mar 2013 (actual)
- Primary completion
- 30 Sep 2021 (actual)
- Study completion
- 30 Sep 2021 (actual)
- First posted
- 26 Oct 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.
U-Net-based architectures will be applied to 500 contrast-enhanced axial MR images of different patients from a single institution after manual segmentation of meningioma, of which 50 were used for testing. Tumor volumetry after autosegmentation by trained U-Net-based architecture is final goal.
Detailed description
U-Net-based architectures will be applied to 500 contrast-enhanced axial MR images of different patients from a single institution after manual segmentation of meningioma, of which 50 were used for testing. After preprocessing with Z-isotropification and intensity normalization of images, 3 U-Net-based networks (2D U-Net, Attention U-Net, 3D U-Net) and 3 nnU-Net-based networks (2D nnU-Net, Attention nnU-Net, 3D nnU-Net) will be trained with meningioma-segmented images. For applying to 3D networks, sagittal and coronal images will be reconstructed using axial images. After prediction, the cut-off of the probability function, which is a trade-off, will be obtained with the Gaussian Mixture Modeling algorithm using the probability density function. The voxels having a probability function higher than that will be finally predicted as meningioma. Tumor volume is calculated as the sum of the product of segmented area and thickness of axial images. For performance evaluation, dice similarity coefficient (DSC), precision, and recall will be evaluated compared with manually segmented voxels for validation datasets. The results of volumetry of each model will be compared with manual segmentation-based volume through Pearson's correlation analysis.
Study design
- Observational model
- Case only
- Time perspective
- Retrospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Meningioma patients | – | Other: Observation |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- Intracranial meningioma patients who were diagnosed by MRI are study population of this study. Inclusion in this study have not been decided according to whether or not surgery for tumor resection was performed or MRI thickness and magnetic power.
Key inclusion criteria (1 listed)
- Radiologically diagnosed meningioma by MRI
Key exclusion criteria (4 listed)
- under 18 years old
- Multiple meningiomas
- Orbital meningioma
- Any prior treatment for intracranial meningioma before registration
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 |
|---|---|---|
| ObservationThis study does not involve any intervention to subjects. | Other | Meningioma patients |
Primary outcome
Accuracy compared with ground truth
Time frame: 10-01-2020 until 09-30-2021
As a primary endpoint, we will examine the ability of U-Net and nnU-Net to segment meningioma in brain MR compared with ground truth. Ground truth is defined as area on MR drawn by two neurosurgeons. Accuracy of autosegmentation of meningioma will be assessed in dice similarity coefficient, recall, and precision.
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 26 Oct 2021.
- Individual participant dataSharing planned: No. We have no plan to share IPD
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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.
The record lists no contact. Use the official record to reach the sponsor.
Questions about this trial
What is this clinical trial studying?
An observational study of Meningioma and Artificial Intelligence, run by Seoul National University Hospital.
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 1 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: finished, as of the sponsor's last update on 26 Oct 2021. Status can change between updates, so confirm with the study contact before making plans.
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?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Accuracy compared with ground truth (time frame: 10-01-2020 until 09-30-2021). 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 26 Oct 2021.