Diagnostic Yield of Deep Learning Based Denoising MRI in Cushing's Disease

May 12, 2024 updated by: Ho Sung Kim, Asan Medical Center

Prospective Observational Study of Diagnostic Yield in Cushing's Disease Using Deep Learning Based Denoising MRI

Negative MRI findings may occur in up to 40% of cases of ACTH producing microadenomas. The aim of the study is to evaluate if detection of ACTH producing microadenomas can be increased using deep learning based denoising MRI.

Study Overview

Status

Terminated

Conditions

Intervention / Treatment

Detailed Description

Detecting ACTH producing microadenoma in MRI is important in establishing the diagnosis of Cushing disease and may enable patients to avoid additional diagnostic tests such as inferior petrosal sinus sampling. However, detecting ACTH producing microadenoma in MRI remains as a diagnostic challenge due its small size with its median diameter of 5-mm. Many attempts have been made in order to improve the sensitivity of detecting ACTH producing microadenoma. It is generally accepted as standard clinical practice to perform dynamic contrast enhanced T1 weighted image to delineate delayed enhancing microadenonoma in comparison to the background enhancement of the normal gland. Despite these attempts, negative MRI findings may occur in up to 40% of cases of ACTH producing microadenomas and there is a need to improve its detection rate. Theoretically, performing thin slice thickness scans should help detecting the lesion but this is unavoidably accompanied with increased level of noise. Deep learning based denoising algorithm can be applied to reduce the noise level and potentially increase the detection rate of ACTH producing microadenomas. The aim of the study is to evaluate if detection of ACTH producing microadenomas can be increased using deep learning based denoising MRI.

Study Type

Observational

Enrollment (Actual)

15

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Locations

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

18 years and older (Adult, Older Adult)

Accepts Healthy Volunteers

N/A

Sampling Method

Non-Probability Sample

Study Population

Patients of tertiary hospital center

Description

Inclusion Criteria:

  • Patients suspected of Cushing disease undergoing MRI
  • Signed informed consent

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

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

Cohorts and Interventions

Group / Cohort
Intervention / Treatment
Denoising MRI group
Patients suspected of Cushing disease undergoing deep learning based denoising MRI
Diagnostic test: MRI

1 mm slice thickness with deep learning based reconstruction algorithm applied to the following sequences:

  • Coronal T2 weighted imaging
  • Dynamic contrast enhanced T1 weighted imaging
  • Coronal contrast enhanced T1 weighted imaging

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Detection rate of ACTH producing microadenoma
Time Frame: 2 months
Proportion of positive MRI with visible microadenoma as percentage (%)
2 months

Secondary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Proportion of patients undergoing additional diagnostic tests
Time Frame: 6 months
Proportion of patients undergoing additional diagnostic tests as percentage (%)
6 months

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Asan Medical Center

Investigators

  • Principal Investigator: Ho Sung Kim, MD PhD, Asan Medical Center

Publications and helpful links

The person responsible for entering information about the study voluntarily provides these publications. These may be about anything related to the study.

General Publications

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Actual)

January 10, 2020

Primary Completion (Actual)

February 28, 2023

Study Completion (Actual)

February 28, 2023

Study Registration Dates

First Submitted

October 8, 2019

First Submitted That Met QC Criteria

October 8, 2019

First Posted (Actual)

October 10, 2019

Study Record Updates

Last Update Posted (Actual)

May 14, 2024

Last Update Submitted That Met QC Criteria

May 12, 2024

Last Verified

May 1, 2024

More Information

Terms related to this study

Additional Relevant MeSH Terms

Other Study ID Numbers

  • AsanMCHSKim_04

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

UNDECIDED

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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