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- Lindberg, K., et al.
(författare)
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Three-Dimensional Volumetric Segmentation of Pituitary Tumors: Assessment of Inter-rater Agreement and Comparison with Conventional Geometric Equations
- 2018
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Ingår i: Journal of Neurological Surgery Part B-Skull Base. - : Georg Thieme Verlag KG. - 2193-6331 .- 2193-634X. ; 79:5, s. 475-481
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Tidskriftsartikel (refereegranskat)abstract
- Background The assessment of pituitary tumor (PT) volume is important in the treatment and follow-up of patients with PT. Previously, PT volume estimation has been performed by conventional geometric equations (CGE) such as abc/2 (simplified ellipsoid volume equation) and 4 pi r(3)/3 (sphere), both presuming a symmetric tumor shape, which occurs uncommonly in patients with PT. In contrast, three-dimensional (3D) voxel-based software segmentation takes the irregular and asymmetric shapes that PTs often possess into account and might be a more accurate method for PT volume segmentation. The purpose of this study is twofold. (1) To compare 3D segmentation with CGE for PT volume estimation. (2) To assess inter-rater reliability in 3D segmentation of PTs. Methods Nineteen high-resolution (1mm slice thickness) T1-weighted MRI examinations of patients with PT were independently analyzed and manually segmented, using the software ITK-SNAP, by two certified neuroradiologists. Concurrently, the volumes of the PTs were estimated with abc/2 and 4 pi r(3)/3 by a clinician, and the results were compared with the corresponding segmented volumes. Results There was a significant decrease in PT volume attained from the segmentations compared with the calculations made with abc/2 (p < 0.001, mean volume 18% higher than segmentation) and 4 pi r(3)/3 (p < 0.001, mean volume 28% higher than segmentation). The intraclass correlation coefficient (ICC) for the two sets of segmented PTs was 0.99. Conclusion CGE (abc/2 and 4 pi r(3)/3) significantly overestimates PT volume compared with 3D volumetric segmentation. The inter-rater agreement on manual 3D volumetric software segmentation is excellent.
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