MRI-Based Radiomics Signature: A Potential Biomarker for Identifying Glypican 3-Positive Hepatocellular Carcinoma | |
Gu, Dongsheng1; Xie, Yongsheng3; Wei, Jingwei1; Li, Wencui2; Ye, Zhaoxiang2; Zhu, Zhongyuan2; Tian, Jie1,3,4,5; Li, Xubin2 | |
刊名 | JOURNAL OF MAGNETIC RESONANCE IMAGING |
2020-06-03 | |
卷号 | 52期号:6页码:1679-1687 |
关键词 | glypican 3 hepatocellular carcinoma radiomics noninvasive nomogram |
ISSN号 | 1053-1807 |
DOI | 10.1002/jmri.27199 |
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. |
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关键词 | DEVELOPMENTALLY-REGULATED TRANSCRIPT ; TEXTURE ANALYSIS ; MARKER ; EXPRESSION ; LIVER ; RECURRENCE ; PREDICTION ; CANCER ; CELLS ; SERUM |
WOS研究方向 | Radiology, Nuclear Medicine & Medical Imaging |
语种 | 英语 |
出版者 | WILEY |
WOS记录号 | WOS:000539400200001 |
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 |
内容类型 | 期刊论文 |
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.Chinese Acad Sci, Inst Automat, Key Lab Mol Imaging, Beijing, Peoples R China 2.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 3.Univ Chinese Acad Sci, Beijing, Peoples R China 4.Xidian Univ, Sch Life Sci & Technol, Minist Educ, Engn Res Ctr Mol & Neuro Imaging, Xian, Peoples R China 5.Beihang Univ, Beijing Adv Innovat Ctr Big Data Based Precis Med, Sch Med, Beijing, 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,2020,52(6):1679-1687. |
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,52(6),1679-1687. |
MLA | Gu, Dongsheng,et al."MRI-Based Radiomics Signature: A Potential Biomarker for Identifying Glypican 3-Positive Hepatocellular Carcinoma".JOURNAL OF MAGNETIC RESONANCE IMAGING 52.6(2020):1679-1687. |
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