Literature DB >> 31254065

Volumetric assessment of glioblastoma and its predictive value for survival.

Christian Henker1, Marie Cristin Hiepel2, Thomas Kriesen2, Moritz Scherer3, Änne Glass4, Christel Herold-Mende3, Martin Bendszus5, Sönke Langner6, Marc-André Weber6, Björn Schneider7, Andreas Unterberg3, Jürgen Piek2.   

Abstract

BACKGROUND: The objective of this study was to evaluate the morphology of glioblastoma on structural pretreatment magnetic resonance imaging (MRI), defining imaging prognostic factors.
METHOD: We conducted a retrospective analysis of MR images from 114 patients harboring a primary glioblastoma, derived from two neurosurgical departments. Tumor segmentation was carried out in a semi-automated fashion. Tumor compartments comprised contrast-enhancing volume (CEV+), perifocal hyperintensity on fluid-attenuated inversion recovery (FLAIR) images (FLAIR+) excluding CEV+, and a non-enhancing area within the CEV+ lesion (CEV-). Additionally, two ratios were calculated from these volumes, the edema-tumor ratio (ETR) and necrosis-tumor ratio (NTR). All patients received surgical resection, followed by concomitant radiation and chemotherapy.
RESULTS: Tumor segmentation revealed the strongest correlation between the CEV+ volume and the CEV-, presenting intratumoral necrosis (p < 0.001). The relation between the tumor surrounding the FLAIR+ area and the CEV+ volume and the ETR is inversely correlated (p = 0.001). The most important prognostic factor in multivariable analysis was NTR (HR 2.63, p = 0.016). The cut-off value in our cohort for NTR was 0.33, equivalent to a decrease in survival if the necrotic core of the tumor (CEV-) accounts for more than 33% of the tumor mass itself (CEV+).
CONCLUSIONS: Our data emphasizes the importance of the necrosis-tumor ratio as a biomarker in glioblastoma imaging, rather than single tumor compartment volumes. NTR can help to identify a subset of tumors with a higher resistance to therapy and a dismal prognosis.

Entities:  

Keywords:  Glioblastoma; Magnetic resonance imaging; Necrosis; Neuroimaging; Prognosis; Survival

Mesh:

Year:  2019        PMID: 31254065     DOI: 10.1007/s00701-019-03966-6

Source DB:  PubMed          Journal:  Acta Neurochir (Wien)        ISSN: 0001-6268            Impact factor:   2.216


  5 in total

1.  On the Prognosis of Multifocal Glioblastoma: An Evaluation Incorporating Volumetric MRI.

Authors:  Johannes Kasper; Nicole Hilbert; Tim Wende; Michael Karl Fehrenbach; Florian Wilhelmy; Katja Jähne; Clara Frydrychowicz; Gordian Hamerla; Jürgen Meixensberger; Felix Arlt
Journal:  Curr Oncol       Date:  2021-04-07       Impact factor: 3.677

2.  Deep learning for glioblastoma segmentation using preoperative magnetic resonance imaging identifies volumetric features associated with survival.

Authors:  Yizhou Wan; Roushanak Rahmat; Stephen J Price
Journal:  Acta Neurochir (Wien)       Date:  2020-07-13       Impact factor: 2.216

Review 3.  State-of-the-art imaging for glioma surgery.

Authors:  Niels Verburg; Philip C de Witt Hamer
Journal:  Neurosurg Rev       Date:  2020-06-30       Impact factor: 3.042

Review 4.  Advanced Imaging Techniques for Newly Diagnosed and Recurrent Gliomas.

Authors:  Luis R Carrete; Jacob S Young; Soonmee Cha
Journal:  Front Neurosci       Date:  2022-02-23       Impact factor: 4.677

5.  Ellipsoid calculations versus manual tumor delineations for glioblastoma tumor volume evaluation.

Authors:  Clara Le Fèvre; Roger Sun; Hélène Cebula; Alicia Thiery; Delphine Antoni; Roland Schott; François Proust; Jean-Marc Constans; Georges Noël
Journal:  Sci Rep       Date:  2022-06-22       Impact factor: 4.996

  5 in total

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