Clinical trial NCT04121988: Diagnostic Yield of Deep Learning Based Denoising MRI in Cushing's Disease
Official title: Prospective Observational Study of Diagnostic Yield in Cushing's Disease Using Deep Learning Based Denoising MRI
Study type: OBSERVATIONAL
Overall status: TERMINATED
Conditions: Pituitary ACTH Secreting Adenoma
Lead sponsor: Asan Medical Center
Sponsor class: OTHER
First posted: 2019-10-10
Enrollment: 15
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Observational model: COHORT
Time perspective: PROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: Patients of tertiary hospital center

Brief summary: 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.

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.

Eligibility criteria: 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

Primary outcomes:
measure: Detection rate of ACTH producing microadenoma
timeFrame: 2 months
Secondary outcomes:
measure: Proportion of patients undergoing additional diagnostic tests
timeFrame: 6 months
Other outcomes:
Locations:
city: Seoul
country: Korea, Republic of
facility: Asan Medical Center
Study officials:
affiliation: Asan Medical Center
name: Ho Sung Kim, MD PhD
role: PRINCIPAL_INVESTIGATOR
References:
citation: 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.
citation: 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 23;130(2):347-351. doi: 10.3171/2017.9.JNS171969. Print 2019 Feb 1.