Abstracts of the International Skeletal Society (ISS) 2020, Virtual Meeting Supplement
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ABSTRACTS
Abstracts of the International Skeletal Society (ISS) 2020, Virtual Meeting Supplement
Breakout Session Wednesday, October 7, 2020 Podium 1: WITHDRAWN
Conclusion: Radiomic features of bone cartilaginous tumors extracted from 2D and 3D segmentations on CT and MRI examinations are reproducible, although some degree of interobserver segmentation variability exists and highlights the need for reliability analysis in radiomic studies. Margin shrinkage did not improve feature robustness compared to contour-focused segmentation.
13:00 – 14:00 Podium 3 Podium 2 EFFECTS OF INTEROBSERVER VARIABILITY ON 2D AND 3D CT- AND MRI-BASED TEXTURE FEATURE REPRODUCIBILITY OF CARTILAGINOUS BONE TUMORS Salvatore Gitto1; Renato Cuocolo2; Ilaria Emili1; Laura Tofanelli1; Vito Chianca3; Domenico Albano3,4; Carmelo Messina1,3; Luca Maria Sconfienza1,3 1 Università degli Studi di Milano, Milan, Italy; 2Università degli Studi di Napoli Federico II, Naples, Italy; 3IRCCS Istituto Ortopedico Galeazzi, Milan, Italy; 4Università degli Studi di Palermo, Palermo, Italy Purpose: To investigate the influence of interobserver manual segmentation variability on the reproducibility of bidimensional (2D) and volumetric (3D) unenhanced computed tomography (CT)- and magnetic resonance imaging (MRI)-based texture analysis. Materials and Methods: This retrospective study included 30 patients with cartilaginous bone tumors (10 enchondromas, 10 atypical cartilaginous tumors and 10 intermediate-to-high grade conventional chondrosarcomas). Three radiologists independently performed manual contour-focused segmentation on unenhanced CT, T1-weighted and T2weighted MRI by drawing both a 2D region of interest (ROI) on the slice showing the largest tumor area and a 3D ROI including the whole tumor volume. Additionally, a marginal erosion was applied to both 2D and 3D segmentations to evaluate the influence of segmentation margins on feature reproducibility. A total of 783 and 1132 features were extracted from original and filtered images and volumes, respectively. Intraclass correlation coefficient (ICC)≥0.75 indicated good-to-excellent interobserver reliability and defined feature stability. Results: In 2D contour-focused vs. margin shrinkage segmentation, the rates of stable features were 74.7% (585) vs. 71.7% (561), 77.1% (604) vs. 76.1% (596) and 95.7% (749) vs. 96.4% (755) for CT, T1weighted and T2-weighted images, respectively (P=0.343). In 3D contour-focused vs. margin shrinkage segmentation, they were 86.6% (980) vs. 83.7% (947), 80.0% (906) vs. 71.5% (809) and 95.0% (1075) vs. 65.7% (744) for CT, T1-weighted and T2weighted volumes, respectively (P
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