Radiomics analysis enables recurrence prediction for hepatocellular carcinoma after liver transplantation

doi:10.1016/j.ejrad.2019.05.010

	Radiomics analysis enables recurrence prediction for hepatocellular carcinoma after liver transplantation
	Guo, Donghui 1; Gu, Dongsheng3 ; Wang, Honghai 2; Wei, Jingwei3 ; Wang, Zhenglu 2; Hao, Xiaohan 3; Ji, Qian 2; Cao, Shunqi 1; Song, Zhuolun 2; Jiang, Jiabing 1; Shen, Zhongyang 2; Tian, Jie 3; Zheng, Hong 2
发表期刊	EUROPEAN JOURNAL OF RADIOLOGY
ISSN	0720-048X
	2019-08-01
卷号	117 页码:33-40
通讯作者	Tian, Jie(tian@ieee.org) ; Zheng, Hong(zhenghong1965@tmu.edu.cn)
摘要	Objectives: To assess whether radiomics signature can identify aggressive behavior and predict recurrence of hepatocellular carcinoma (HCC) after liver transplantation. Methods: Our study consisted of a training dataset (n = 93) and a validation dataset (40) with clinically confirmed HCC after liver transplantation from October 2011 to December 2016. Radiomics features were extracted by delineating regions-of-interest (ROIs) around the lesion in four phases of CT images. A radiomics signature was generated using the least absolute shrinkage and selection operator (LASSO) Cox regression model. The association between radiomics signature and recurrence-free survival (RFS) was assessed. Preoperative clinical characteristics potentially associated with RFS were evaluated to develop a clinical model. A combined model incorporating clinical risk factors and radiomics signature was built. Results: The stable radiomics features associated with the recurrence of HCC were simply found in arterial phase and portal phase. The prediction model based on the radiomics features extracted from the arterial phase showed better prediction performance than the portal vein phase or the fusion signature combining both of arterial and portal vein phase. A radiomics nomogram based on combined model consisting of the radiomics signature and clinical risk factors showed good predictive performance for RFS with a C-index of 0.785 (95% confidence interval [CI]: 0.674-0.895) in the training dataset and 0.789 (95% CI: 0.620-0.957) in the validation dataset. The calibration curves showed agreement in both training (p = 0.121) and validation cohorts (p = 0.164). Conclusions: Radiomics signature extracted from CT images may be a potential imaging biomarker for liver cancer invasion and enable accurate prediction of HCC recurrence after liver transplantation.
关键词	Artificial intelligence Hepatocellular carcinoma Liver transplantation Recurrence
DOI	10.1016/j.ejrad.2019.05.010
关键词[WOS]	CT TEXTURE ANALYSIS ; MICROVASCULAR INVASION ; TUMOR HETEROGENEITY ; SURGICAL RESECTION ; POTENTIAL MARKER ; IMAGING FEATURES ; SURVIVAL ; CANCER ; IMAGES ; CHEMOTHERAPY
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China[81227901] ; National Natural Science Foundation of China[81527805] ; National Natural Science Foundation of China[61231004] ; National Natural Science Foundation of China[81771924] ; National Natural Science Foundation of China[81501616] ; National Key R&D Program of China[2017YFA0205200] ; National Key R&D Program of China[2017YFC1308700] ; Science and Technology Service Network Initiative of the Chinese Academy of Sciences[KFJ-SW-STS-160] ; Tianjin Clinical Research Center for Organ Transplantation Project[15ZXLCSY00070] ; National Natural Science Foundation of China[81227901] ; National Natural Science Foundation of China[81527805] ; National Natural Science Foundation of China[61231004] ; National Natural Science Foundation of China[81771924] ; National Natural Science Foundation of China[81501616] ; National Key R&D Program of China[2017YFA0205200] ; National Key R&D Program of China[2017YFC1308700] ; Science and Technology Service Network Initiative of the Chinese Academy of Sciences[KFJ-SW-STS-160] ; Tianjin Clinical Research Center for Organ Transplantation Project[15ZXLCSY00070]
项目资助者	National Natural Science Foundation of China ; National Key R&D Program of China ; Science and Technology Service Network Initiative of the Chinese Academy of Sciences ; Tianjin Clinical Research Center for Organ Transplantation Project
WOS研究方向	Radiology, Nuclear Medicine & Medical Imaging
WOS类目	Radiology, Nuclear Medicine & Medical Imaging
WOS记录号	WOS:000475337200005
出版者	ELSEVIER IRELAND LTD
七大方向——子方向分类	医学影像处理与分析
引用统计	被引频次：53[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/26853
专题	中国科学院分子影像重点实验室
通讯作者	Tian, Jie; Zheng, Hong
作者单位	1.Tianjin Med Univ, Cent Clin Coll 1, Tianjin 300192, Peoples R China 2.Tianjin First Cent Hosp, Oriental Organ Transplant Ctr, 24 Fukang Rd, Tianjin 300192, Peoples R China 3.Chinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, Beijing 100190, Peoples R China
通讯作者单位	中国科学院分子影像重点实验室
推荐引用方式 GB/T 7714	Guo, Donghui,Gu, Dongsheng,Wang, Honghai,et al. Radiomics analysis enables recurrence prediction for hepatocellular carcinoma after liver transplantation[J]. EUROPEAN JOURNAL OF RADIOLOGY,2019,117:33-40.
APA	Guo, Donghui.,Gu, Dongsheng.,Wang, Honghai.,Wei, Jingwei.,Wang, Zhenglu.,...&Zheng, Hong.(2019).Radiomics analysis enables recurrence prediction for hepatocellular carcinoma after liver transplantation.EUROPEAN JOURNAL OF RADIOLOGY,117,33-40.
MLA	Guo, Donghui,et al."Radiomics analysis enables recurrence prediction for hepatocellular carcinoma after liver transplantation".EUROPEAN JOURNAL OF RADIOLOGY 117(2019):33-40.