Automatic Segmentation MRI Cerebral Glioma

December 14, 2020 updated by: Fatma Rabiee Ali Sedeek, Assiut University

The Added Value of Automatic Segmentation of Cerebral Gliomas in Multi-Sequence Magnetic Resonance Imaging (MRI)

The aim of this study is to evaluate the role of automatic segmentation of cerebral gliomas in multi-sequence MR images using state-of-the-art methods for automatic segmentation and internal classification of brain tumors in correlation with operative findings

Study Overview

Status

Not yet recruiting

Conditions

Intervention / Treatment

Detailed Description

Gliomas are the most common primary brain tumors and are classified by their histopathological appearances using the World Health Organization (WHO) system into low-grade glioma (LGG) (grades I and II) and high-grade glioma (grade III anaplastic glioma and grade IV glioblastoma.

Gliomas, particularly high-grade, exhibit irregular growth patterns infiltrating the surrounding brain and thus showing irregular boundaries that may not be clear on conventional magnetic resonance images (MRI) MR images are visually inspected by radiologists, however, visual assessment is subjective, time consuming and prone to variability due to inter-rater differences. Accurate delineation of tumor boundaries as well as assessment of tumor volume are essential for treatment planning and monitoring treatment response . However, accurate delineation of the boundaries of glioma using subjective visual assessment is often difficult due to tumor heterogeneity and complexity, overlapping signal intensity with surrounding tissues and uneven tumor growth into nearby structures .

Compared to tumor volumetry, the routine visual evaluation of tumor size is based upon simple linear measurements of the gross tumor volume. These bi-dimensional measurements are often performed on a single MRI slice without volumetric measurements. These linear measurements are user-dependent and prone to errors due to increased measurement variability, especially in irregularly shaped lesions Computer-based fully-automatic tumor segmentation methods present a possible solution to these issues. The process is based upon information extraction from structural brain MRI images using a probabilistic tissue model to define the clear tumor boundaries using different MRI pulse sequences. These methods could accurately and rapidly identify glioma from surrounding normal brain tissue, and perform tumor volumetry, while eliminating intra-observer and inter-observer variability Internal changes within glioma, such as enhancement pattern and degeneration are crucial for identification of glioma grade, planning of treatment, monitoring of disease progression and evaluating the efficacy of therapy. In the process of automatic glioma segmentation, different parts of the glioma are characterized as solid (active) tumor, necrosis and peri-tumoral edema .

Automatic segmentation methods utilize artificial intelligence and machine learning techniques for extraction of information from multi-sequence MRI including, basically, T1W, Gadolinium enhanced T1W, T2W and FLAIR sequences .

Appropriate assessment of the extent of tumor resection plays an important role in the prognosis of glioma, since maximizing the extent of resection influences survival in these patients. Complete resection of enhancing tumor, defined as the removal of the final 1-2% of the tumor, seems to provide the most benefit in terms of patient's survival . Automatic segmentation could lead to better diagnosis and proper treatment planning through accurate tumor localization and classification .

Study Type

Observational

Enrollment (Anticipated)

50

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Contact

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

  • Child
  • Adult
  • Older Adult

Accepts Healthy Volunteers

No

Genders Eligible for Study

All

Sampling Method

Non-Probability Sample

Study Population

The study will include 50 patients with cerebral gliomas identified by MRI A standardized multi-parametric MR protocol will be implemented for all patients. All sequences will be acquired on a 1.5T MR scanner.

Description

Inclusion Criteria:

- Patients with cerebral gliomas identified by MRI who will be treated surgically

Exclusion Criteria:

  • Previously operated or biopsied gliomas.

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

  • Observational Models: Cohort
  • Time Perspectives: Prospective

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
evaluate the role of automatic segmentation of cerebral gliomas in multi-sequence MR images in correlation with operative findings.
Time Frame: baseline
The aim of this study is to evaluate the role of automatic segmentation of cerebral gliomas in multi-sequence MR images using state-of-the-art methods for automatic segmentation and internal classification of brain tumors in correlation with operative findings.
baseline

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Assiut University

Investigators

  • Study Director: Mostafa Mostafa, Assiut University
  • Study Director: Hosameldeen Metwalli, Assiut University
  • Study Director: Noha Attia, Assiut University
  • Principal Investigator: fatma sedeek, Assiut University

Publications and helpful links

The person responsible for entering information about the study voluntarily provides these publications. These may be about anything related to the study.

General Publications

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Anticipated)

January 1, 2021

Primary Completion (Anticipated)

February 1, 2023

Study Completion (Anticipated)

April 1, 2023

Study Registration Dates

First Submitted

December 14, 2020

First Submitted That Met QC Criteria

December 14, 2020

First Posted (Actual)

December 19, 2020

Study Record Updates

Last Update Posted (Actual)

December 19, 2020

Last Update Submitted That Met QC Criteria

December 14, 2020

Last Verified

December 1, 2020

More Information

Terms related to this study

Keywords

Additional Relevant MeSH Terms

Other Study ID Numbers

  • MRI cerebral glioma

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

product manufactured in and exported from the U.S.

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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