Automatic Segmentation MRI Cerebral Glioma
The Added Value of Automatic Segmentation of Cerebral Gliomas in Multi-Sequence Magnetic Resonance Imaging (MRI)
1 other identifier
observational
50
0 countries
N/A
Brief Summary
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
Trial Health
Trial Health Score
Automated assessment based on enrollment pace, timeline, and geographic reach
participants targeted
Target at P25-P50 for all trials
Started Jan 2021
Typical duration for all trials
Health score is calculated from publicly available data and should be used for screening purposes only.
Trial Relationships
Click on a node to explore related trials.
Study Timeline
Key milestones and dates
First Submitted
Initial submission to the registry
December 14, 2020
CompletedFirst Posted
Study publicly available on registry
December 19, 2020
CompletedStudy Start
First participant enrolled
January 1, 2021
CompletedPrimary Completion
Last participant's last visit for primary outcome
February 1, 2023
CompletedStudy Completion
Last participant's last visit for all outcomes
April 1, 2023
CompletedDecember 19, 2020
December 1, 2020
2.1 years
December 14, 2020
December 14, 2020
Conditions
Keywords
Outcome Measures
Primary Outcomes (1)
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.
baseline
Interventions
magnetic resonance imaging
Eligibility Criteria
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.
You may qualify if:
- \- Patients with cerebral gliomas identified by MRI who will be treated surgically
You may not qualify if:
- Previously operated or biopsied gliomas.
Contact the study team to confirm eligibility.
Sponsors & Collaborators
Related Publications (9)
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.
PMID: 27651330BACKGROUNDWen 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.
PMID: 20231676BACKGROUNDNiyazi 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.
PMID: 26777122BACKGROUNDTabatabai 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.
PMID: 20862485BACKGROUNDKanaly 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.
PMID: 25036205BACKGROUNDMeier 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.
PMID: 27001047BACKGROUNDGordillo 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.
PMID: 23790354BACKGROUNDPorz 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.
PMID: 27806121BACKGROUNDNaceur 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.
PMID: 30415717BACKGROUND
Study Officials
- STUDY DIRECTOR
Mostafa Mostafa
Assiut University
- STUDY DIRECTOR
Hosameldeen Metwalli
Assiut University
- STUDY DIRECTOR
Noha Attia
Assiut University
- PRINCIPAL INVESTIGATOR
fatma sedeek
Assiut University
Central Study Contacts
Study Design
- Study Type
- observational
- Observational Model
- COHORT
- Time Perspective
- PROSPECTIVE
- Sponsor Type
- OTHER
- Responsible Party
- PRINCIPAL INVESTIGATOR
- PI Title
- assuit egypt
Study Record Dates
First Submitted
December 14, 2020
First Posted
December 19, 2020
Study Start
January 1, 2021
Primary Completion
February 1, 2023
Study Completion
April 1, 2023
Last Updated
December 19, 2020
Record last verified: 2020-12