Cerebral GlioblastomaNCT04674579
Automatic Segmentation MRI Cerebral Glioma
An observational study of Cerebral Glioblastoma, run by Assiut University.
Trial 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 Read more about this study
- Who is it for?
- Any age
- Conditions the study is for
- Cerebral Glioblastoma
- What is being tested?
- MRI
- Type of treatment
- Medical device
- How many people
- 50 participants
- Where
- No sites listed yet
- Study dates
- 1 Jan 2021to Apr 2023
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages Any age
- All sexes
- Child, Adult, Older adult
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.
Who may be able to join
- Patients with cerebral gliomas identified by MRI who will be treated surgically
Who cannot join
- Previously operated or biopsied gliomas.
Trial locations and contacts
No sites are listed yet.
About this study
Official title: 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
Read the 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 .
- Conditions
- Cerebral Glioblastoma
- Being tested
- MRI
- Keywords
- MRI automatic segmentation
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
MRIDevice
magnetic resonance imaging
What is being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: baseline
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted19 Dec 2020
- Study start1 Jan 2021 (estimated)
- Primary completionFeb 2023 (estimated)
- Study completionApr 2023 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (9)Papers the sponsor has linked to this study.
- Other trials for Cerebral GlioblastomaStudies of the same condition, open and under way.
- More trials run by Assiut UniversityThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Prospective
- Enrolment
- 50 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Assiut University
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Fatma Rabiee Ali Sedeek, Assiut University
- Investigators
- Mostafa Mostafa, Study director, Assiut UniversityHosameldeen Metwalli, Study director, Assiut UniversityNoha Attia, Study director, Assiut Universityfatma sedeek, Principal investigator, Assiut University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT04674579
- Sponsor's protocol ID
- MRI cerebral glioma
- Status last verified
- Dec 2020
- First submitted
- 14 Dec 2020
- Last updated
- 19 Dec 2020
- Results posted
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
Publications
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.
PubMed 27651330Wen 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.
PubMed 20231676Niyazi 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.
PubMed 26777122Tabatabai 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.
PubMed 20862485Kanaly 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.
PubMed 25036205Meier 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.
PubMed 27001047Gordillo 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.
PubMed 23790354Porz 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.
PubMed 27806121Naceur 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.
PubMed 30415717
Study at a glance
From the registry record, last updated by the sponsor on 19 Dec 2020. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- The Added Value of Automatic Segmentation of Cerebral Gliomas in Multi-Sequence Magnetic Resonance Imaging (MRI)
- ClinicalTrials.gov ID
- NCT04674579
- Acronym / sponsor ID
- MRI cerebral glioma
- Conditions
- Cerebral Glioblastoma
- Keywords
- MRI automatic segmentation
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 50 participants (estimated)
- Lead sponsor
- Assiut UniversityUniversity, hospital or other
- Study start
- 1 Jan 2021 (estimated)
- Primary completion
- Feb 2023 (estimated)
- Study completion
- Apr 2023 (estimated)
- First posted
- 19 Dec 2020 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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
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 design
- Observational model
- Cohort
- Time perspective
- Prospective
Who may qualify
- Sex
- All
- Age groups
- Child, Adult, Older adult
- 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.
Key inclusion criteria (1 listed)
- Patients with cerebral gliomas identified by MRI who will be treated surgically
Key exclusion criteria (1 listed)
- Previously operated or biopsied gliomas.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| MRImagnetic resonance imaging | Device | – |
Primary outcome
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.
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 19 Dec 2020.
- Background: PubMed 27651330Soltaninejad 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.
- Background: PubMed 20231676Wen 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.
- Background: PubMed 26777122Niyazi 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.
- Background: PubMed 20862485Tabatabai 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.
- Background: PubMed 25036205Kanaly 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.
- Background: PubMed 27001047Meier 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.
- Background: PubMed 23790354Gordillo 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.
- Background: PubMed 27806121Porz 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.
- Background: PubMed 30415717Naceur 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.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Mostafa Mostafa study director
Assiut University
Hosameldeen Metwalli study director
Assiut University
Noha Attia study director
Assiut University
Questions about this trial
What is this clinical trial studying?
An observational study of Cerebral Glioblastoma, run by Assiut University.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 1 inclusion and 1 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: status not confirmed, as of the sponsor's last update on 19 Dec 2020. Status can change between updates, so confirm with the study contact before making plans.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
Contact fatma sedeek (01066952726, fatma.rabiee15@gmail.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
evaluate the role of automatic segmentation of cerebral gliomas in multi-sequence MR images in correlation with operative findings. (time frame: baseline). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT04674579, which also lists 9 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 19 Dec 2020.