Effects of Interobserver Variability on 2D and 3D CT- and MRI-Based Texture Feature Reproducibility of Cartilaginous Bone Tumors

• Published in: Journal of digital imaging Vol. 34; no. 4; pp. 820 - 832
• Main Authors: Gitto, Salvatore, Cuocolo, Renato, Emili, Ilaria, Tofanelli, Laura, Chianca, Vito, Albano, Domenico, Messina, Carmelo, Imbriaco, Massimo, Sconfienza, Luca Maria
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01.08.2021; Springer Nature B.V
• Subjects: Artificial intelligence; Bone cancer; Bone imaging; Bone tumors; Computed tomography; Contour matching; Correlation coefficient; Correlation coefficients; Feature extraction; Image processing; Image segmentation; Imaging; Magnetic resonance imaging; Medical imaging; Medicine; Medicine & Public Health; Radiology; Radiomics; Reliability analysis; Reproducibility; Stability; Texture; Tumors; Chondrosarcoma; Texture analysis; Chondroma; Artificial intelligence; Neoplasms; Radiomics
• ISSN: 0897-1889; 1618-727X
• DOI: 10.1007/s10278-021-00498-3

This study aims to investigate the influence of interobserver manual segmentation variability on the reproducibility of 2D and 3D unenhanced computed tomography (CT)- and magnetic resonance imaging (MRI)-based texture analysis. Thirty patients with cartilaginous bone tumors (10 enchondromas, 10 atypical cartilaginous tumors, 10 chondrosarcomas) were retrospectively included. Three radiologists independently performed manual contour-focused segmentation on unenhanced CT and T1-weighted and T2-weighted 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. A total of 783 and 1132 features were extracted from original and filtered 2D and 3D images, respectively. Intraclass correlation coefficient ≥ 0.75 defined feature stability. In 2D vs. 3D contour-focused segmentation, the rates of stable features were 74.71% vs. 86.57% ( p  < 0.001), 77.14% vs. 80.04% ( p  = 0.142), and 95.66% vs. 94.97% ( p  = 0.554) for CT and T1-weighted and T2-weighted images, respectively. Margin shrinkage did not improve 2D ( p  = 0.343) and performed worse than 3D ( p  < 0.001) contour-focused segmentation in terms of feature stability. In 2D vs. 3D contour-focused segmentation, matching stable features derived from CT and MRI were 65.8% vs. 68.7% ( p  = 0.191), and those derived from T1-weighted and T2-weighted images were 76.0% vs. 78.2% ( p  = 0.285). 2D and 3D radiomic features of cartilaginous bone tumors extracted from unenhanced CT and MRI are reproducible, although some degree of interobserver segmentation variability highlights the need for reliability analysis in future studies.