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

Asan Medical Center

Status: Terminated

Conditions: Pituitary ACTH Secreting Adenoma

Treatments: Diagnostic Test: MRI

Study type: Observational

Funder types: Other

Identifiers

NCT04121988

AsanMCHSKim_04

Details and patient eligibility

About

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.

Full 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.

Enrollment: 15 patients

Sex: All

Ages: 18+ years old

Volunteers: No Healthy Volunteers

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

Trial design

15 participants in 1 patient group

Denoising MRI group

Description:

Patients suspected of Cushing disease undergoing deep learning based denoising MRI

Treatment:

Diagnostic Test: MRI