Literature DB >> 30210975

Three-Dimensional Volumetric Segmentation of Pituitary Tumors: Assessment of Inter-rater Agreement and Comparison with Conventional Geometric Equations.

Karl Lindberg1,2, Angelica Kouti2, Doerthe Ziegelitz2, Tobias Hallén1, Thomas Skoglund1,3, Dan Farahmand1,3.   

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 4πr 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 4πr 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 4πr 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 4πr 3 /3 ) significantly overestimates PT volume compared with 3D volumetric segmentation. The inter-rater agreement on manual 3D volumetric software segmentation is excellent.

Entities:  

Keywords:  abc/2; pituitary tumor volume; segmentation; volumetric analysis

Year:  2018        PMID: 30210975      PMCID: PMC6133660          DOI: 10.1055/s-0037-1618577

Source DB:  PubMed          Journal:  J Neurol Surg B Skull Base        ISSN: 2193-634X


  37 in total

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