Automated Segmentation and Volumetry for Meningioma Using Deep Learning

NCT05093751 | Completed

• 1 other identifier: SNUH-MNG-AI001
• Study Type: observational
• Target: 600
• Locations: 0 countries
• Sites: N/A

Brief Summary

U-Net-based architectures will be applied to 500 contrast-enhanced axial MR images of different patients from a single institution after manual segmentation of meningioma, of which 50 were used for testing. Tumor volumetry after autosegmentation by trained U-Net-based architecture is final goal.

Conditions

Meningioma; Artificial Intelligence

Keywords

Meningioma; Artificial intelligence; autosegmentation; volumetry

Outcome Measures

Primary Outcomes (1)

• Accuracy compared with ground truth

  As a primary endpoint, we will examine the ability of U-Net and nnU-Net to segment meningioma in brain MR compared with ground truth. Ground truth is defined as area on MR drawn by two neurosurgeons. Accuracy of autosegmentation of meningioma will be assessed in dice similarity coefficient, recall, and precision.

  10-01-2020 until 09-30-2021

Study Arms (1)

Meningioma patients

Other: Observation

Interventions

Observation OTHER

This study does not involve any intervention to subjects.

Meningioma patients

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Intracranial meningioma patients who were diagnosed by MRI are study population of this study. Inclusion in this study have not been decided according to whether or not surgery for tumor resection was performed or MRI thickness and magnetic power.

You may qualify if:

• Radiologically diagnosed meningioma by MRI

You may not qualify if:

• under 18 years old
• Multiple meningiomas
• Orbital meningioma
• Any prior treatment for intracranial meningioma before registration

Sponsors & Collaborators

• Seoul National University Hospital: lead

MeSH Terms

Conditions

Meningioma

Interventions

Observation

Condition Hierarchy (Ancestors)

Neoplasms, Nerve Tissue; Neoplasms by Histologic Type; Neoplasms; Neoplasms, Vascular Tissue; Meningeal Neoplasms; Central Nervous System Neoplasms; Nervous System Neoplasms; Neoplasms by Site; Nervous System Diseases

Intervention Hierarchy (Ancestors)

Methods; Investigative Techniques

Study Design

• Study Type: observational
• Observational Model: CASE ONLY
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Professor

Study Record Dates

• First Submitted: October 1, 2021
• First Posted: October 26, 2021
• Study Start: March 23, 2013
• Primary Completion: September 30, 2021
• Study Completion: September 30, 2021
• Last Updated: October 26, 2021

Record last verified: 2021-10

Data Sharing

• IPD Sharing: Will not share