Intra-rater variability in low-grade glioma segmentation

Abstract

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.

Glioma imaging in Europe: A survey of 220 centres and recommendations for best clinical practice

“…The debate about whether volumetric glioma measurements would be more accurate than linear measurements and/or would impact clinical management is ongoing, especially for the response assessment of lower grade gliomas, which may be challenged by subtle growth. Conflicting results exist regarding the reliability of low-grade glioma segmentations [66, 67]. An additional hurdle is that currently available semi-automated volumetric segmentation algorithms tend to require manual editing [67–70].…”

“…Conflicting results exist regarding the reliability of low-grade glioma segmentations [66, 67]. An additional hurdle is that currently available semi-automated volumetric segmentation algorithms tend to require manual editing [67–70]. …”

Is the anatomical distribution of low-grade gliomas linked to regions of gliogenesis?

“…A radiologist experienced with LGG assessment and segmentation (H.K.B.) performed semi-automatic tumor segmentation as previously described [13]. Segmentations were performed in the open source software 3D Slicer 4.4.0 (https ://www.slice r.org), a software platform for quantitative imaging [14].…”

Texture analysis of computed tomography data using morphologic and metabolic delineation of esophageal cancer—relation to tumor type and neoadjuvant therapy response

“…Study I. A radiologist experienced with LGG assessment and segmentation performed the semiquantitative data interpretation and segmented the tumors using an open source software called 3D Slicer (http://www.slicer.org/) applying a previously described method (102,103). The radiologist was blinded for clinical result and molecular status while evaluating contrast enhancement (no, patchy, nodular and ring-like), corpus callosum involvement (yes, no), tumor borders, main tumor side, volume in milliliters, and mass effect (no, mild, conspicuous).…”