Artikel
Semiautomatic image segmentation based volume approximation of intracranial meningiomas
Semiautomatische Volumenapproximation von intrakraniellen Meningiomen
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Veröffentlicht: | 25. Mai 2022 |
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Gliederung
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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.