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, Dongsheng 3; Wang, Honghai 2; Wei, Jingwei3 ; Wang, Zhenglu 2; Hao, Xiaohan 3; Ji, Qian 2; Cao, Shunqi 1; Song, Zhuolun 2; Jiang, Jiabing 1
刊名	EUROPEAN JOURNAL OF RADIOLOGY
	2019-08-01
卷号	117 页码:33-40
关键词	Artificial intelligence Hepatocellular carcinoma Liver transplantation Recurrence
ISSN号	0720-048X
DOI	10.1016/j.ejrad.2019.05.010
通讯作者	Tian, Jie(tian@ieee.org) ; Zheng, Hong(zhenghong1965@tmu.edu.cn)
英文摘要	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.
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]
WOS关键词	CT TEXTURE ANALYSIS ; MICROVASCULAR INVASION ; TUMOR HETEROGENEITY ; SURGICAL RESECTION ; POTENTIAL MARKER ; IMAGING FEATURES ; SURVIVAL ; CANCER ; IMAGES ; CHEMOTHERAPY
WOS研究方向	Radiology, Nuclear Medicine & Medical Imaging
语种	英语
出版者	ELSEVIER IRELAND LTD
WOS记录号	WOS:000475337200005
资助机构	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
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/26852]
专题	中国科学院自动化研究所
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
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.