gms | German Medical Science

73. Jahrestagung der Deutschen Gesellschaft für Neurochirurgie (DGNC)
Joint Meeting mit der Griechischen Gesellschaft für Neurochirurgie

Deutsche Gesellschaft für Neurochirurgie (DGNC) e. V.

29.05. - 01.06.2022, Köln

Semiautomatic image segmentation based volume approximation of intracranial meningiomas

Semiautomatische Volumenapproximation von intrakraniellen Meningiomen

Meeting Abstract

Suche in Medline nach

  • Martin Kauke - Universitätsklinikum Köln, Klinik für Neurochirurgie, Köln, Deutschland
  • Ali Safi - Universitätsklinikum Köln, Klinik für Neurochirurgie, Köln, Deutschland
  • Roland Goldbrunner - Universitätsklinikum Köln, Klinik für Neurochirurgie, Köln, Deutschland
  • presenting/speaker Marco Timmer - Universitätsklinikum Köln, Klinik für Neurochirurgie, Köln, Deutschland

Deutsche Gesellschaft für Neurochirurgie. 73. Jahrestagung der Deutschen Gesellschaft für Neurochirurgie (DGNC), Joint Meeting mit der Griechischen Gesellschaft für Neurochirurgie. Köln, 29.05.-01.06.2022. Düsseldorf: German Medical Science GMS Publishing House; 2022. DocP169

doi: 10.3205/22dgnc481, urn:nbn:de:0183-22dgnc4817

Veröffentlicht: 25. Mai 2022

© 2022 Kauke et al.
Dieser Artikel ist ein Open-Access-Artikel und steht unter den Lizenzbedingungen der Creative Commons Attribution 4.0 License (Namensnennung). Lizenz-Angaben siehe http://creativecommons.org/licenses/by/4.0/.


Gliederung

Text

Objective: Management of meningioma patients greatly depends on the extent of clinical disease manifestation as well as tumor shape and size. We introduce an easy usable and open access semi-automatic image segmentation for volumetric and morphometric brain tumor analysis.

Methods: ITK-SNAP was utilized for semi-automatic image segmentation of 58 gadolinium-contrast enhanced T1-weighted thin-slice MRI datasets for volumetric and morphometric analysis. Furthermore, multimodal imaging datasets (including T2, FLAIR, T1) were evaluated for radiological biomarkers of aggressiveness and growth potential. Patient charts were retrospectively reviewed for accessory clinico-pathologic data including clinical disease manifestation, age, sex, weight, tumor grade and location. The statistical impact of various parameters was determined by Student’s t-test.

Results: Semiautomatic image segmentation at hand of a thin-slice gadolinium contrast-enhanced T1-weighted image series is feasible, freely accessible and enables quick volume approximation. Location (p=0.001), clinical disease manifestation (p=0.033), peritumoral edema (p= 0.038), tumor intrinsic cystic degeneration (p=0.007), multifocality (p=0.022) and meningioma mass effect (p=0.001) were statistically associated with higher volumes, irrespective of tumor grade.

Conclusion: Conventional methods for size characterization of meningiomas neglect a large portion of an MRI-scans dataset. Hence, this paper is intended to propagate the usage of semi-automatic image segmentation for size characterization and morphometric analysis of meningiomas. Additionally, we highlight the importance of correct size indication by means of segmentation based volume approximation, particularly in the context of accurate neoplastic disease state quantification for patient observation and follow up.