Evaluation of Cavernous Sinus Invasion by Pituitary Adenoma Using Deep Learning Based Denoising MR
Primary Purpose
Cavernous Sinus Invasion by Pituitary Adenoma
Status
Unknown status
Phase
Not Applicable
Locations
Korea, Republic of
Study Type
Interventional
Intervention
MRI with deep learning based denoising
Sponsored by
About this trial
This is an interventional diagnostic trial for Cavernous Sinus Invasion by Pituitary Adenoma
Eligibility Criteria
Inclusion Criteria:
- Patients undergoing preoperative brain MR for pituitary adenoma
Exclusion Criteria:
- 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
Sites / Locations
- Asan Medical CenterRecruiting
Arms of the Study
Arm 1
Arm Type
Experimental
Arm Label
Deep learning based denoising MR
Arm Description
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
Outcomes
Primary Outcome Measures
Cavernous sinus invasion
Presence or absence of cavernous sinus invasion determined surgically
Secondary Outcome Measures
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
Margin of pituitary adenoma (well-delineated, poorly delineated) on the MRI
Margin of pituitary adenoma (well-delineated, poorly delineated) on the MRI
Full Information
NCT ID
NCT04268251
First Posted
January 13, 2020
Last Updated
February 19, 2020
Sponsor
Asan Medical Center
1. Study Identification
Unique Protocol Identification Number
NCT04268251
Brief Title
Evaluation of Cavernous Sinus Invasion by Pituitary Adenoma Using Deep Learning Based Denoising MR
Official Title
Prospective MRI Evaluation of Cavernous Sinus Invasion by Pituitary Adenoma Using Deep Learning Based Denoising
Study Type
Interventional
2. Study Status
Record Verification Date
February 2020
Overall Recruitment Status
Unknown status
Study Start Date
January 12, 2020 (Actual)
Primary Completion Date
February 28, 2021 (Anticipated)
Study Completion Date
February 28, 2021 (Anticipated)
3. Sponsor/Collaborators
Responsible Party, by Official Title
Principal Investigator
Name of the Sponsor
Asan Medical Center
4. Oversight
Studies a U.S. FDA-regulated Drug Product
No
Studies a U.S. FDA-regulated Device Product
No
5. Study Description
Brief 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. 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.
6. Conditions and Keywords
Primary Disease or Condition Being Studied in the Trial, or the Focus of the Study
Cavernous Sinus Invasion by Pituitary Adenoma
7. Study Design
Primary Purpose
Diagnostic
Study Phase
Not Applicable
Interventional Study Model
Single Group Assignment
Masking
None (Open Label)
Allocation
N/A
Enrollment
76 (Anticipated)
8. Arms, Groups, and Interventions
Arm Title
Deep learning based denoising MR
Arm Type
Experimental
Arm Description
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
Intervention Type
Diagnostic Test
Intervention Name(s)
MRI with deep learning based denoising
Intervention Description
1-mm coronal contrast-enhanced T1 weighted image with deep learning based denoising
Primary Outcome Measure Information:
Title
Cavernous sinus invasion
Description
Presence or absence of cavernous sinus invasion determined surgically
Time Frame
Within 1 week
Secondary Outcome Measure Information:
Title
Size of pituitary adenoma (in mm), laterality of pituitary adenoma (unilateral or bilateral) on the MRI
Description
Size of the tumor (in mm), laterality of the tumor (unilateral or bilateral) on the MRI
Time Frame
Within 1 week
Title
Margin of pituitary adenoma (well-delineated, poorly delineated) on the MRI
Description
Margin of pituitary adenoma (well-delineated, poorly delineated) on the MRI
Time Frame
Within 1 week
10. Eligibility
Sex
All
Minimum Age & Unit of Time
18 Years
Accepts Healthy Volunteers
No
Eligibility Criteria
Inclusion Criteria:
Patients undergoing preoperative brain MR for pituitary adenoma
Exclusion Criteria:
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
Central Contact Person:
First Name & Middle Initial & Last Name or Official Title & Degree
Ho Sung Kim, MD PhD
Phone
+82 2 3010 5682
Email
radhskim@gmail.com
First Name & Middle Initial & Last Name or Official Title & Degree
Minjae Kim, MD
Phone
+82 2 3010 0187
Email
manzae.kim@gmail.com
Overall Study Officials:
First Name & Middle Initial & Last Name & Degree
Ho Sung Kim, MD PhD
Organizational Affiliation
Asan Medical Center
Official's Role
Principal Investigator
Facility Information:
Facility Name
Asan Medical Center
City
Seoul
Country
Korea, Republic of
Individual Site Status
Recruiting
Facility Contact:
First Name & Middle Initial & Last Name & Degree
Minjae Kim, MD
Phone
+82 2 3010 0187
Email
manzae.kim@gmail.com
12. IPD Sharing Statement
Plan to Share IPD
No
Learn more about this trial
Evaluation of Cavernous Sinus Invasion by Pituitary Adenoma Using Deep Learning Based Denoising MR
We'll reach out to this number within 24 hrs