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CompletedNCT04268251Updated May 14, 2024

Evaluation of Cavernous Sinus Invasion by Pituitary Adenoma Using Deep Learning Based Denoising MR

An interventional study of MRI with deep learning based denoising in Cavernous Sinus Invasion by Pituitary Adenoma, sponsored by Asan Medical Center. Completed at 1 site in South Korea. Open to participants aged 18 Years and older. Per ClinicalTrials.gov, last updated 2024-05-14.

Sponsored by Asan Medical Center · Not applicable, Interventional, and Diagnostic

Phase
Not applicable
Study type
Interventional
Enrollment
67
Allocation
Not applicable
Ages
18 Years and older
Sex
All
01

Study 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.

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Conditions studied

  • Cavernous Sinus Invasion by Pituitary Adenoma
03

In context

Lead sponsor

Asan Medical Center is the lead sponsor of 578 studies on the registry; 74 are open to participants now.

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Counted across the registry records on this site, refreshed daily.

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Who can participate

Ages eligible
18 Years and older
Sexes eligible
All
Accepts healthy volunteers
No

Go through the criteria one by one — yes, no or not sure — and take the list to your doctor or the study team. Kept in this browser only.

Inclusion criteria

  • Patients undergoing preoperative brain MR for pituitary adenoma

Exclusion criteria

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
05

Study design

Phase
Not applicable
Primary purpose
Diagnostic
Allocation
Not applicable
Intervention model
Single group
Masking
None (open label)
Enrollment
67 participants (actual)

Study arms

  • Experimental
    Deep learning based denoising MR

    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

    Diagnostic Test: MRI with deep learning based denoising

Interventions

  • Diagnostic testMRI with deep learning based denoising

    1-mm coronal contrast-enhanced T1 weighted image with deep learning based denoising

06

What researchers measure

Primary outcomes

  1. Cavernous sinus invasion

    Presence or absence of cavernous sinus invasion determined surgically

    Time frame: Within 1 week

Secondary outcomes

  1. 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

    Time frame: Within 1 week

  2. Margin of pituitary adenoma (well-delineated, poorly delineated) on the MRI

    Margin of pituitary adenoma (well-delineated, poorly delineated) on the MRI

    Time frame: Within 1 week

07

Study locations

1 site
  • Asan Medical Center
    Seoul, South Korea
08

References and documents

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 ↗

Individual participant data

Plan to share: No

09

Timeline

Tracking since Sep 25, 2026
ElapsedEstimatedEstimated, not yet reached

Ran from Jan 2020 to primary completion in Aug 2020, 7 months.

How these figures are worked out, and which studies are left out of them.

Registered
Jan 13, 2020
First posted
Feb 13, 2020
StartActual
Jan 12, 2020
Primary completionActual
Aug 30, 2020
CompletionActual
Feb 28, 2022
Last update
May 14, 2024
What these dates mean
Start.
When the first participant enrolled; while estimated, when the study expects to open to recruitment.
Primary completion.
When the last participant was examined or treated to collect data for the study's main question, its primary outcome. A study that stopped early gives the date its data collection stopped.
Completion.
When the last participant's last visit took place, for every outcome, secondary ones and side effects included: the end of the study. Often later than primary completion, sometimes the same day.
Actual and Est.
Actual means the registry says the date has happened. Est. (estimated) is a date the sponsor plans or expects. It can still move, and the sponsor is expected to change it to actual once it has passed.
No changes since tracking began. The registry record was last updated on May 14, 2024, before this site started recording changes on Sep 25, 2026. Its history is on ClinicalTrials.gov ↗
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Registry details

Key details

Study ID
NCT04268251
Lead sponsor
Asan Medical Center
Responsible party
Ho Sung Kim (Professor, Asan Medical Center) — Principal investigator
First posted
Feb 13, 2020
Start date
Jan 12, 2020
Primary completion
Aug 30, 2020
Completion
Feb 28, 2022
Last update
May 14, 2024

Study contacts

Ho Sung Kim, MD PhD
principal investigator · Asan Medical Center

Oversight

FDA-regulated drug
No
FDA-regulated device
No
View the source record on ClinicalTrials.gov ↗

Not currently enrolling

This study is completed, as verified in May 2024. You cannot join it, but the record below documents what was studied.

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