CASIA OpenIR
Radiomics analysis enables recurrence prediction for hepatocellular carcinoma after liver transplantation
Guo, Donghui1; Gu, Dongsheng3; Wang, Honghai2; Wei, Jingwei3; Wang, Zhenglu2; Hao, Xiaohan3; Ji, Qian2; Cao, Shunqi1; Song, Zhuolun2; Jiang, Jiabing1; Shen, Zhongyang2; Tian, Jie3; Zheng, Hong2
Source PublicationEUROPEAN JOURNAL OF RADIOLOGY
ISSN0720-048X
2019-08-01
Volume117Pages:33-40
Corresponding AuthorTian, Jie(tian@ieee.org) ; Zheng, Hong(zhenghong1965@tmu.edu.cn)
AbstractObjectives: 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.
KeywordArtificial intelligence Hepatocellular carcinoma Liver transplantation Recurrence
DOI10.1016/j.ejrad.2019.05.010
WOS KeywordCT TEXTURE ANALYSIS ; MICROVASCULAR INVASION ; TUMOR HETEROGENEITY ; SURGICAL RESECTION ; POTENTIAL MARKER ; IMAGING FEATURES ; SURVIVAL ; CANCER ; IMAGES ; CHEMOTHERAPY
Indexed BySCI
Language英语
Funding ProjectNational 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]
Funding OrganizationNational 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 Research AreaRadiology, Nuclear Medicine & Medical Imaging
WOS SubjectRadiology, Nuclear Medicine & Medical Imaging
WOS IDWOS:000475337200005
PublisherELSEVIER IRELAND LTD
Citation statistics
Cited Times:4[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/26853
Collection中国科学院自动化研究所
Corresponding AuthorTian, Jie; Zheng, Hong
Affiliation1.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
Corresponding Author AffilicationChinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, Beijing 100190, Peoples R China
Recommended Citation
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.
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