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MRI-Based Radiomics Signature: A Potential Biomarker for Identifying Glypican 3-Positive Hepatocellular Carcinoma

文献类型：期刊论文

作者Gu, Dongsheng4; Xie, Yongsheng2; Wei, Jingwei4; Li, Wencui3; Ye, Zhaoxiang3; Zhu, Zhongyuan3; Tian, Jie1,2,4,5; Li, Xubin3
刊名JOURNAL OF MAGNETIC RESONANCE IMAGING
出版日期2020-06-03
页码9
关键词glypican 3 hepatocellular carcinoma radiomics noninvasive nomogram
ISSN号1053-1807
DOI10.1002/jmri.27199
通讯作者Tian, Jie(tian@ieee.org) ; Li, Xubin(lixb@bjmu.edu.cn)
英文摘要Background Glypican 3 (GPC3) expression has proved to be a critical risk factor related to prognosis in hepatocellular carcinoma (HCC) patients. Purpose To investigate the performance of MRI-based radiomics signature in identifying GPC3-positive HCC. Study Type Retrospective. Population An initial cohort of 293 patients with pathologically confirmed HCC was involved in this study, and patients were randomly divided into training (195) and validation (98) cohorts. Field Strength/Sequences Contrast-enhanced T-1-weight MRI was performed with a 1.5T scanner. Assessment A total of 853 radiomic features were extracted from the volume imaging. Univariate analysis and Fisher scoring were utilized for feature reduction. Subsequently, forward stepwise feature selection and radiomics signature building were performed based on a support vector machine (SVM). Incorporating independent risk factors, a combined nomogram was developed by multivariable logistic regression modeling. Statistical Tests The predictive performance of the nomogram was calculated using the area under the receive operating characteristic curve (AUC). Decision curve analysis (DCA) was applied to estimate the clinical usefulness. Results The radiomics signature consisting of 10 selected features achieved good prediction efficacy (training cohort: AUC = 0.879, validation cohort: AUC = 0.871). Additionally, the combined nomogram integrating independent clinical risk factor alpha-fetoprotein (AFP) and radiomics signature showed improved calibration and prominent predictive performance with AUCs of 0.926 and 0.914 in the training and validation cohorts, respectively. Data Conclusion The proposed MR-based radiomics signature is strongly related to GPC3-positive. The combined nomogram incorporating AFP and radiomics signature may provide an effective tool for noninvasive and individualized prediction of GPC3-positive in patients with HCC.
WOS关键词DEVELOPMENTALLY-REGULATED TRANSCRIPT ; TEXTURE ANALYSIS ; MARKER ; EXPRESSION ; LIVER ; RECURRENCE ; PREDICTION ; CANCER ; CELLS ; SERUM
资助项目National Natural Science Foundation of China[81227901] ; National Natural Science Foundation of China[81527805] ; Ministry of Science and Technology of China[2017YFC1308701] ; Ministry of Science and Technology of China[2017YFC1309100] ; Ministry of Science and Technology of China[2016YFC0102600] ; Ministry of Science and Technology of China[2016YFA0100902] ; Ministry of Science and Technology of China[2016YFC0103803] ; Ministry of Science and Technology of China[2016YFA0201401] ; Ministry of Science and Technology of China[2016YFC0103702] ; Ministry of Science and Technology of China[2014CB748600] ; Ministry of Science and Technology of China[2016YFC0103001] ; Chinese Academy of Sciences[GJJSTD20170004] ; Chinese Academy of Sciences[QYZDJ-SSW-JSC005] ; Beijing Municipal Science & Technology Commission[Z161100002616022] ; Beijing Municipal Science & Technology Commission[Z171100000117023] ; Strategic Priority Research Program of Chinese Academy of Science[XDBS01000000]
WOS研究方向Radiology, Nuclear Medicine & Medical Imaging
语种英语
WOS记录号WOS:000539400200001
出版者WILEY
资助机构National Natural Science Foundation of China ; Ministry of Science and Technology of China ; Chinese Academy of Sciences ; Beijing Municipal Science & Technology Commission ; Strategic Priority Research Program of Chinese Academy of Science
源URL[http://ir.ia.ac.cn/handle/173211/39768]  
专题自动化研究所_中国科学院分子影像重点实验室
通讯作者Tian, Jie; Li, Xubin
作者单位1.Beihang Univ, Beijing Adv Innovat Ctr Big Data Based Precis Med, Sch Med, Beijing, Peoples R China
2.Univ Chinese Acad Sci, Beijing, Peoples R China
3.Tianjin Med Univ Canc Inst & Hosp, Dept Radiol, Natl Clin Res Ctr Canc, Tianjins Clin Res Ctr Canc,Key Lab Canc Prevent &, Tianjin, Peoples R China
4.Chinese Acad Sci, Inst Automat, Key Lab Mol Imaging, Beijing, Peoples R China
5.Xidian Univ, Sch Life Sci & Technol, Minist Educ, Engn Res Ctr Mol & Neuro Imaging, Xian, Peoples R China
推荐引用方式
GB/T 7714		 Gu, Dongsheng,Xie, Yongsheng,Wei, Jingwei,et al. MRI-Based Radiomics Signature: A Potential Biomarker for Identifying Glypican 3-Positive Hepatocellular Carcinoma[J]. JOURNAL OF MAGNETIC RESONANCE IMAGING,2020:9.
APA Gu, Dongsheng.,Xie, Yongsheng.,Wei, Jingwei.,Li, Wencui.,Ye, Zhaoxiang.,...&Li, Xubin.(2020).MRI-Based Radiomics Signature: A Potential Biomarker for Identifying Glypican 3-Positive Hepatocellular Carcinoma.JOURNAL OF MAGNETIC RESONANCE IMAGING,9.
MLA Gu, Dongsheng,et al."MRI-Based Radiomics Signature: A Potential Biomarker for Identifying Glypican 3-Positive Hepatocellular Carcinoma".JOURNAL OF MAGNETIC RESONANCE IMAGING (2020):9.

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