Study Results
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Basic Information
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UNKNOWN
50 participants
OBSERVATIONAL
2021-01-01
2023-04-30
Brief Summary
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Detailed Description
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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 .
Conditions
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Keywords
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Study Design
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COHORT
PROSPECTIVE
Interventions
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MRI
magnetic resonance imaging
Eligibility Criteria
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Inclusion Criteria
Exclusion Criteria
ALL
No
Sponsors
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Assiut University
OTHER
Responsible Party
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Fatma Rabiee Ali Sedeek
assuit egypt
Principal Investigators
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Mostafa Mostafa
Role: STUDY_DIRECTOR
Assiut University
Hosameldeen Metwalli
Role: STUDY_DIRECTOR
Assiut University
Noha Attia
Role: STUDY_DIRECTOR
Assiut University
fatma sedeek
Role: PRINCIPAL_INVESTIGATOR
Assiut University
Central Contacts
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References
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Soltaninejad M, Yang G, Lambrou T, Allinson N, Jones TL, Barrick TR, Howe FA, Ye X. Automated brain tumour detection and segmentation using superpixel-based extremely randomized trees in FLAIR MRI. Int J Comput Assist Radiol Surg. 2017 Feb;12(2):183-203. doi: 10.1007/s11548-016-1483-3. Epub 2016 Sep 20.
Wen PY, Macdonald DR, Reardon DA, Cloughesy TF, Sorensen AG, Galanis E, Degroot J, Wick W, Gilbert MR, Lassman AB, Tsien C, Mikkelsen T, Wong ET, Chamberlain MC, Stupp R, Lamborn KR, Vogelbaum MA, van den Bent MJ, Chang SM. Updated response assessment criteria for high-grade gliomas: response assessment in neuro-oncology working group. J Clin Oncol. 2010 Apr 10;28(11):1963-72. doi: 10.1200/JCO.2009.26.3541. Epub 2010 Mar 15.
Niyazi M, Brada M, Chalmers AJ, Combs SE, Erridge SC, Fiorentino A, Grosu AL, Lagerwaard FJ, Minniti G, Mirimanoff RO, Ricardi U, Short SC, Weber DC, Belka C. ESTRO-ACROP guideline "target delineation of glioblastomas". Radiother Oncol. 2016 Jan;118(1):35-42. doi: 10.1016/j.radonc.2015.12.003. Epub 2016 Jan 6.
Tabatabai G, Stupp R, van den Bent MJ, Hegi ME, Tonn JC, Wick W, Weller M. Molecular diagnostics of gliomas: the clinical perspective. Acta Neuropathol. 2010 Nov;120(5):585-92. doi: 10.1007/s00401-010-0750-6. Epub 2010 Sep 23.
Kanaly CW, Mehta AI, Ding D, Hoang JK, Kranz PG, Herndon JE 2nd, Coan A, Crocker I, Waller AF, Friedman AH, Reardon DA, Sampson JH. A novel, reproducible, and objective method for volumetric magnetic resonance imaging assessment of enhancing glioblastoma. J Neurosurg. 2014 Sep;121(3):536-42. doi: 10.3171/2014.4.JNS121952. Epub 2014 Jul 18.
Meier R, Knecht U, Loosli T, Bauer S, Slotboom J, Wiest R, Reyes M. Clinical Evaluation of a Fully-automatic Segmentation Method for Longitudinal Brain Tumor Volumetry. Sci Rep. 2016 Mar 22;6:23376. doi: 10.1038/srep23376.
Gordillo N, Montseny E, Sobrevilla P. State of the art survey on MRI brain tumor segmentation. Magn Reson Imaging. 2013 Oct;31(8):1426-38. doi: 10.1016/j.mri.2013.05.002. Epub 2013 Jun 20.
Porz N, Habegger S, Meier R, Verma R, Jilch A, Fichtner J, Knecht U, Radina C, Schucht P, Beck J, Raabe A, Slotboom J, Reyes M, Wiest R. Fully Automated Enhanced Tumor Compartmentalization: Man vs. Machine Reloaded. PLoS One. 2016 Nov 2;11(11):e0165302. doi: 10.1371/journal.pone.0165302. eCollection 2016.
Naceur MB, Saouli R, Akil M, Kachouri R. Fully Automatic Brain Tumor Segmentation using End-To-End Incremental Deep Neural Networks in MRI images. Comput Methods Programs Biomed. 2018 Nov;166:39-49. doi: 10.1016/j.cmpb.2018.09.007. Epub 2018 Sep 21.
Other Identifiers
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MRI cerebral glioma
Identifier Type: -
Identifier Source: org_study_id