How to cite item

Variability of radiomic features extracted from multi-b-value diffusion-weighted images in hepatocellular carcinoma

  
@article{TCR26802,
	author = {Jing Zhang and Qingtao Qiu and Jinghao Duan and Guanzhong Gong and Qingjun Jiang and Gang Sun and Yong Yin},
	title = {Variability of radiomic features extracted from multi-b-value diffusion-weighted images in hepatocellular carcinoma},
	journal = {Translational Cancer Research},
	volume = {8},
	number = {1},
	year = {2019},
	keywords = {},
	abstract = {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/mm2: 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/mm2 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 },
	issn = {2219-6803},	url = {http://tcr.amegroups.com/article/view/26802}
}