Cavernous Sinus Invasion by Pituitary AdenomaNCT04268251
Evaluation of Cavernous Sinus Invasion by Pituitary Adenoma Using Deep Learning Based Denoising MR
A study testing MRI with deep learning based denoising in people with Cavernous Sinus Invasion by Pituitary Adenoma, run by Asan Medical Center at 1 site in South Korea.
Trial summary
Preoperative evaluation of cavernous sinus invasion by pituitary adenoma is critical for performing safe operation and deciding on surgical extent as well as for treatment success. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Cavernous Sinus Invasion by Pituitary Adenoma
- What is being tested?
- MRI with deep learning based denoising
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 67 participants
- Where
- 1 siteSouth Korea
- Study dates
- 12 Jan 2020to 28 Feb 2022
- Phase
- Interventional
- 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
Who may be able to join
- Patients undergoing preoperative brain MR for pituitary adenoma
Who cannot join
- Patients who have any type of bioimplant activated by mechanical, electronic, or magnetic means (e.g., cochlear implants, pacemakers, neurostimulators, biostimulates, electronic infusion pumps, etc), because such devices may be displaced or malfunction
- Patients who are pregnant or breast feeding; urine pregnancy test will be performed on women of child bearing potential
- Poor MRI image quality due to artifacts
Trial locations and contacts
1 site in 1 country.
About this study
Official title: Prospective MRI Evaluation of Cavernous Sinus Invasion by Pituitary Adenoma Using Deep Learning Based Denoising
Preoperative evaluation of cavernous sinus invasion by pituitary adenoma is critical for performing safe operation and deciding on surgical extent as well as for treatment success. Because of the small size of the pituitary gland and sellar fossa, determining the exact relationship between the pituitary adenoma and cavernous sinus can be challenging. Performing thin slice thickness MRI may be beneficial but is inevitably associated with increased noise level. By applying deep learning based denoising algorithm, diagnosis of cavernous sinus invasion by pituitary adenoma may be improved.
- Being tested
- MRI with deep learning based denoising
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
MRI with deep learning based denoisingDiagnostic test
1-mm coronal contrast-enhanced T1 weighted image with deep learning based denoising
| Study group | Role | What they receive |
|---|---|---|
| Deep learning based denoising MR | Experimental | 1 mm slice thickness coronal contrast-enhanced T1 weighted imaging with deep learning based denoising vs. 3 mm slice thickness coronal contrast-enhanced T1 weighted imaging Receives: Diagnostic Test: MRI with deep learning based denoising |
What is being measured
The primary outcome is the main question the study is designed to answer.
Cavernous sinus invasion
Presence or absence of cavernous sinus invasion determined surgically
Measured: Within 1 week
Secondary outcomes (2)
Size of pituitary adenoma (in mm), laterality of pituitary adenoma (unilateral or bilateral) on the MRI
Size of the tumor (in mm), laterality of the tumor (unilateral or bilateral) on the MRI
Measured: Within 1 week
Margin of pituitary adenoma (well-delineated, poorly delineated) on the MRI
Margin of pituitary adenoma (well-delineated, poorly delineated) on the MRI
Measured: Within 1 week
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted13 Feb 2020
- Study start12 Jan 2020
- Primary completion30 Aug 2020
- Study completion28 Feb 2022
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 Cavernous Sinus Invasion by Pituitary AdenomaStudies of the same condition, open and under way.
- More trials run by Asan Medical CenterThe 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
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Diagnosis
- Allocation
- Not applicable
- Intervention model
- Single group
- Masking
- None (open label)
- Enrolment
- 67 participants (actual)
Sponsor and investigators
- Lead sponsor
- Asan Medical Center
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Ho Sung Kim, Asan Medical Center
- Investigators
- Ho Sung Kim, MD PhD, Principal investigator, Asan Medical Center
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT04268251
- Sponsor's protocol ID
- AsanMCHSKim_06
- Status last verified
- May 2024
- First submitted
- 13 Jan 2020
- Last updated
- 14 May 2024
- Results posted
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Publications
Kim M, Kim HS, Park JE, Park SY, Kim YH, Kim SJ, Lee J, Lebel MR. Thin-Slice Pituitary MRI with Deep Learning-Based Reconstruction for Preoperative Prediction of Cavernous Sinus Invasion by Pituitary Adenoma: A Prospective Study. AJNR Am J Neuroradiol. 2022 Feb;43(2):280-285. doi: 10.3174/ajnr.A7387. Epub 2022 Jan 6.
PubMed 34992127
Study at a glance
From the registry record, last updated by the sponsor on 14 May 2024. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Prospective MRI Evaluation of Cavernous Sinus Invasion by Pituitary Adenoma Using Deep Learning Based Denoising
- ClinicalTrials.gov ID
- NCT04268251
- Acronym / sponsor ID
- AsanMCHSKim_06
- Study type
- Interventional
- Recruitment status
- Completed
- Enrolment
- 67 participants (actual)
- Lead sponsor
- Asan Medical CenterUniversity, hospital or other
- Study start
- 12 Jan 2020 (actual)
- Primary completion
- 30 Aug 2020 (actual)
- Study completion
- 28 Feb 2022 (actual)
- First posted
- 13 Feb 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.
Preoperative evaluation of cavernous sinus invasion by pituitary adenoma is critical for performing safe operation and deciding on surgical extent as well as for treatment success. Because of the small size of the pituitary gland and sellar fossa, determining the exact relationship between the pituitary adenoma and cavernous sinus can be challenging. Performing thin slice thickness MRI may be beneficial but is inevitably associated with increased noise level. By applying deep learning based denoising algorithm, diagnosis of cavernous sinus invasion by pituitary adenoma may be improved.
Study design
- Primary purpose
- Diagnosis
- Allocation
- Not applicable
- Intervention model
- Single group
- Masking
- None (open label)
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Deep learning based denoising MR | Experimental | Diagnostic Test: MRI with deep learning based denoising1 mm slice thickness coronal contrast-enhanced T1 weighted imaging with deep learning based denoising vs. 3 mm slice thickness coronal contrast-enhanced T1 weighted imaging |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (1 listed)
- Patients undergoing preoperative brain MR for pituitary adenoma
Key exclusion criteria (3 listed)
- Patients who have any type of bioimplant activated by mechanical, electronic, or magnetic means (e.g., cochlear implants, pacemakers, neurostimulators, biostimulates, electronic infusion pumps, etc), because such devices may be displaced or malfunction
- Patients who are pregnant or breast feeding; urine pregnancy test will be performed on women of child bearing potential
- Poor MRI image quality due to artifacts
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 |
|---|---|---|
| MRI with deep learning based denoising1-mm coronal contrast-enhanced T1 weighted image with deep learning based denoising | Diagnostic test | Deep learning based denoising MR |
Primary outcome
Cavernous sinus invasion
Time frame: Within 1 week
Presence or absence of cavernous sinus invasion determined surgically
2 secondary outcomes
Size of pituitary adenoma (in mm), laterality of pituitary adenoma (unilateral or bilateral) on the MRI
Time frame: Within 1 week
Margin of pituitary adenoma (well-delineated, poorly delineated) on the MRI
Time frame: Within 1 week
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 14 May 2024.
- Background: PubMed 34992127Kim M, Kim HS, Park JE, Park SY, Kim YH, Kim SJ, Lee J, Lebel MR. Thin-Slice Pituitary MRI with Deep Learning-Based Reconstruction for Preoperative Prediction of Cavernous Sinus Invasion by Pituitary Adenoma: A Prospective Study. AJNR Am J Neuroradiol. 2022 Feb;43(2):280-285. doi: 10.3174/ajnr.A7387. Epub 2022 Jan 6.
- Individual participant dataSharing planned: No
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.
Ho Sung Kim, MD PhD principal investigator
Asan Medical Center
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing MRI with deep learning based denoising in people with Cavernous Sinus Invasion by Pituitary Adenoma, run by Asan Medical Center at 1 site in South Korea.
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 3 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 14 May 2024. 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 South Korea. 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?
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?
Cavernous sinus invasion (time frame: Within 1 week). 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 NCT04268251, 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 14 May 2024.