Clinical trial · Observational
Automatic Segmentation MRI Cerebral Glioma
The Added Value of Automatic Segmentation of Cerebral Gliomas in Multi-Sequence Magnetic Resonance Imaging (MRI)
NCT04674579CI-TRIAL-00048974unknownClinicalTrials.gov clinicaltrialsProvenance
- Source
- ClinicalTrials.gov
- Retrieved
- Sep 8, 2026
- Layer
- normalized (units and labels harmonized; values unchanged)
- Run
- ING-CLINICALTRIALS-20260908-000001
Summary
Brief summary (as posted)
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
Conditions
Conditions (1)
Free-text conditions as registered, with the CancerIndex entity they were reconciled to and the match type.
| Condition (as posted) | Mapped entity | Match | Confidence |
|---|---|---|---|
| Cerebral Glioblastoma | Cerebral Glioblastoma | ONTOLOGY_EXACT | 0.98 |
Interventions
Interventions (1)
| Intervention | Type | Mapped drug | Match |
|---|---|---|---|
| MRI | Device | — | UNRESOLVED |
Design
Arms and outcomes
Arms (0)
[]Primary outcomes (1)
- measure
- evaluate the role of automatic segmentation of cerebral gliomas in multi-sequence MR images in correlation with operative findings.
- timeFrame
- baseline
- description
- 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.
Eligibility
Eligibility (as posted)
- Sex
- All
Show eligibility criteria text
Inclusion Criteria: \- Patients with cerebral gliomas identified by MRI who will be treated surgically Exclusion Criteria: * Previously operated or biopsied gliomas.
References
Publications (9)
- BACKGROUNDSoltaninejad 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 27651330
- BACKGROUNDWen 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 20231676
- BACKGROUNDNiyazi 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 26777122
- BACKGROUNDTabatabai 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 20862485
- BACKGROUNDKanaly 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 25036205
- BACKGROUNDMeier 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.