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

Study Overview
=================
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.

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

Conditions
-----------------
Pituitary ACTH Secreting Adenoma

Intervention / Treatment
-----------------
* Diagnostic Test: MRI


Participation Criteria
=================
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

Ages Eligible for Study
-----------------
Minimum Age: 18 Years

Sexes Eligible for Study
-----------------
All

Study Plan
=================
How is the study designed?
-----------------

Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Denoising MRI group<br>Patients suspected of Cushing disease undergoing deep learning based denoising MRI | Diagnostic Test: MRI<br>* 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<br>|

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 |