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Reproducibility of radiomic features with GrowCut and GraphCut semiautomatic tumor segmentation in hepatocellular carcinoma

  
@article{TCR16457,
	author = {Qingtao Qiu and Jinghao Duan and Guanzhong Gong and Yukun Lu and Dengwang Li and Jie Lu and Yong Yin},
	title = {Reproducibility of radiomic features with GrowCut and GraphCut semiautomatic tumor segmentation in hepatocellular carcinoma},
	journal = {Translational Cancer Research},
	volume = {6},
	number = {5},
	year = {2017},
	keywords = {},
	abstract = {Background: The reproducibility of radiomic features is a critical challenge facing radiomic models of tumor prediction or prognosis. The aim of this study is to evaluate the reproducibility of radiomic features with the GrowCut and GraphCut semi-automatic tumor segmentation methods in hepatocellular carcinoma (HCC) CT images. 
Methods: Computed tomography (CT) data sets (arterial enhanced phase) of 15 patients with HCC were randomly selected in this study. To acquire the gross tumor volume (GTV), semi-automatic segmentation with the GrowCut and GraphCut methods was implemented in 3D Slicer software by two independent observers. Meanwhile, manual delineation of the GTV was implemented by five abdomen radiation oncologists in this study. We divided the sample into three groups: the GrowCut group, the GraphCut group and manual group. Radiomic features (including tumor intensity histogram-based features, textural features and shape-based features) were extracted using the Imaging Biomarker Explorer (IBEX) software. The intraclass correlation coefficient (ICC) was applied to assess the reproducibility of all radiomic features. 
Results: The radiomic features in the GrowCut group (ICC =0.87±0.19) showed higher reproducibility compared with the radiomic features in the GraphCut group (ICC =0.82±0.24, P},
	issn = {2219-6803},	url = {http://tcr.amegroups.com/article/view/16457}
}