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

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

TERMINATED

Total Enrollment

15 participants

Study Classification

OBSERVATIONAL

Study Start Date

2020-01-10

Study Completion Date

2023-02-28

Brief Summary

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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.

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Detailed Description

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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.

Conditions

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Pituitary ACTH Secreting Adenoma

Study Design

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Observational Model Type

COHORT

Study Time Perspective

PROSPECTIVE

Study Groups

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Denoising MRI group

Patients suspected of Cushing disease undergoing deep learning based denoising MRI

MRI

Intervention Type DIAGNOSTIC_TEST

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

Interventions

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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

Intervention Type DIAGNOSTIC_TEST

Eligibility Criteria

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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

Minimum Eligible Age

18 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

No