TY - JOUR AU - Zhang, Jing AU - Qiu, Qingtao AU - Duan, Jinghao AU - Gong, Guanzhong AU - Jiang, Qingjun AU - Sun, Gang AU - Yin, Yong PY - 2019 TI - Variability of radiomic features extracted from multi-b-value diffusion-weighted images in hepatocellular carcinoma JF - Translational Cancer Research; Vol 8, No 1 (February 28, 2019): Translational Cancer Research Y2 - 2019 KW - N2 - Background: Reliable and meaningful radiomic features is extremely crucial to characterize tumor phenotypes. This study was designed to experimentally evaluate the variability of radiomic features extracted from different b-values diffusion-weighted images (DWIs) in hepatocellular carcinoma (HCC). Methods: The research population was composed of 34 HCC patients and 12 healthy volunteers. At 3.0 T MR scanner, with the identical imaging protocols, all cases underwent the following sequences at 10 b-values ranging from 0 to 1,500 s/mm 2 : T1WI, T2WI, multiple phases contrast-enhanced and intravoxel incoherent motion-DWI scans. For HCC trail, gross tumor volume (GTV) were manually delineated by an experienced radiologist at the b=0 s/mm 2 DWI sequence. For healthy volunteers trail, 3 cylindric regions of interest (ROIs) with 14 mm in height and approximately 20 mm in diameter were defined in parenchyma at II/III, V/VI and VII hepatic segments. Using 3D Slicer Radiomics software (www.slicer.org), we extracted 74 radiomic features, including 19 first-order statistical features and 55 texture features for each case sequence. Percentage coefficient of variation (%COV) was applied to evaluate the stability of each feature and %COV Results: The value of intensity histogram features and texture features derived from DWIs showed a dependency on the b-values in HCC. The low variations (%COV 2 and b=1,000 s/mm 2 ) of DWIs showed a high reproducibility. 12 radiomic features can be used to identify HCC and normal liver. Conclusions: Being influenced by different b-values, radiomic features tested here exist variability in HCC DWIs. Most features are unstable and extremely dependent on b-values in DWIs. Meanwhile, the research revealed that reproducible features can be extracted by nearby b-values DWIs. UR - https://tcr.amegroups.org/article/view/26802