Intra-rater variability in low-grade glioma segmentation

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• 作者：Hans Kristian Bø ; Ole Solheim ; Asgeir Store Jakola…
• 关键词：Glioma ; Segmentation ; Magnetic resonance imaging ; Intra ; rater variability
• 刊名：Journal of Neuro-Oncology
• 出版年：2017
• 出版时间：January 2017
• 年：2017
• 卷：131
• 期：2
• 页码：393-402
• 全文大小：
• 刊物类别：Medicine
• 刊物主题：Oncology; Neurology;
• 出版者：Springer US
• ISSN：1573-7373
• 卷排序：131

文摘

Assessment of size and growth are key radiological factors in low-grade gliomas (LGGs), both for prognostication and treatment evaluation, but the reliability of LGG-segmentation is scarcely studied. With a diffuse and invasive growth pattern, usually without contrast enhancement, these tumors can be difficult to delineate. The aim of this study was to investigate the intra-observer variability in LGG-segmentation for a radiologist without prior segmentation experience. Pre-operative 3D FLAIR images of 23 LGGs were segmented three times in the software 3D Slicer. Tumor volumes were calculated, together with the absolute and relative difference between the segmentations. To quantify the intra-rater variability, we used the Jaccard coefficient comparing both two (J2) and three (J3) segmentations as well as the Hausdorff Distance (HD). The variability measured with J2 improved significantly between the two last segmentations compared to the two first, going from 0.87 to 0.90 (p = 0.04). Between the last two segmentations, larger tumors showed a tendency towards smaller relative volume difference (p = 0.07), while tumors with well-defined borders had significantly less variability measured with both J2 (p = 0.04) and HD (p < 0.01). We found no significant relationship between variability and histological sub-types or Apparent Diffusion Coefficients (ADC). We found that the intra-rater variability can be considerable in serial LGG-segmentation, but the variability seems to decrease with experience and higher grade of border conspicuity. Our findings highlight that some criteria defining tumor borders and progression in 3D volumetric segmentation is needed, if moving from 2D to 3D assessment of size and growth of LGGs.