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

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: Unknown
• Conditions: Pituitary ACTH Secreting Adenoma
• Intervention / Treatment: Diagnostic test: MRI

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 (Anticipated): 64

Contacts and Locations

Study Locations

• Korea, Republic of
  • Seoul, Korea, Republic of
    • Recruiting
    • Asan Medical Center

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: 18 years and older (ADULT, OLDER_ADULT)
• Accepts Healthy Volunteers: N/A
• Genders Eligible for Study: All

• 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

How is the study designed?

Number of groups / cohorts

1

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	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	Proportion of patients undergoing additional diagnostic tests as percentage (%)	6 months

Collaborators and Investigators

• Sponsor: Asan Medical Center

Publications and helpful links

General Publications

• Grober Y, Grober H, Wintermark M, Jane JA, Oldfield EH. Comparison of MRI techniques for detecting microadenomas in Cushing's disease. J Neurosurg. 2018 Apr;128(4):1051-1057. doi: 10.3171/2017.3.JNS163122. Epub 2017 Apr 28.
• Law M, Wang R, Liu CJ, Shiroishi MS, Carmichael JD, Mack WJ, Weiss M, Wang DJJ, Toga AW, Zada G. Value of pituitary gland MRI at 7 T in Cushing's disease and relationship to inferior petrosal sinus sampling: case report. J Neurosurg. 2018 Mar 1:1-5. doi: 10.3171/2017.9.JNS171969. Online ahead of print.

Study record dates

Study Major Dates

• Study Start (ACTUAL): January 10, 2020
• Primary Completion (ANTICIPATED): February 1, 2021
• Study Completion (ANTICIPATED): February 1, 2021

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): January 18, 2020
• Last Update Submitted That Met QC Criteria: January 13, 2020
• Last Verified: January 1, 2020

More Information

Terms related to this study

• Additional Relevant MeSH Terms: Brain Diseases; Central Nervous System Diseases; Nervous System Diseases; Neoplasms by Histologic Type; Neoplasms; Neoplasms by Site; Neoplasms, Glandular and Epithelial; Endocrine System Diseases; Endocrine Gland Neoplasms; Hypothalamic Diseases; Hyperpituitarism; Pituitary Diseases; Pituitary Neoplasms; Adenoma; ACTH-Secreting Pituitary Adenoma; Pituitary ACTH Hypersecretion
• 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