An observational study in Cerebral Glioblastoma, sponsored by Assiut University. Status unknown. Per ClinicalTrials.gov, last updated 2020-12-19.
Sponsored by Assiut University · Observational
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
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 .
1,920 studies on the registry are indexed under Glioblastoma; 450 are open to participants now.
This study's planned enrollment of 50 is below the median of 60 across 188 observational studies indexed under Glioblastoma.
Browse Glioblastoma studies →Assiut University is the lead sponsor of 4,901 studies on the registry; 2,098 are open to participants now.
Of its 13 completed or terminated interventional studies of FDA-regulated products, 0 (0%) have results posted.
Counted across the registry records on this site, refreshed daily.
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.
Exclusion Criteria:
magnetic resonance imaging
evaluate the role of automatic segmentation of cerebral gliomas in multi-sequence MR images in correlation with operative findings.
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.
Time frame: baseline
No study locations are listed for this record.
This study is status unknown, as verified in Dec 2020. You cannot join it, but the record below documents what was studied.
Get an email when the registry record changes — status, dates, results — or when someone posts here.
Sign in to followQuestions and observations about this study, from anyone following it. Not medical advice, and not a channel to the study team — their contact details are on the registry record.
Sign in to join the discussion. Reading takes no account; posting does. You choose a display name, and a pseudonym is the default.
Nothing here yet. If you are running this trial, taking part in it, or weighing whether to, this is the place to say so.
Assiut University