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Predicting microvascular invasion in hepatocellular carcinoma: A dual-institution study on gadoxetate disodium-enhanced MRI.

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机构: [1]Department of Radiology, West China Hospital, Sichuan University, Chengdu, Sichuan, China. [2]Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China. [3]Beijing Key Laboratory of Molecular Imaging, Beijing, China. [4]Department of Medical Imaging, Henan Provincial People's Hospital, Zhengzhou, China. [5]Department of Medical Imaging, People's Hospital of Zhengzhou University, Zhengzhou, China. [6]Department of Liver Surgery & Liver Transplantation, West China Hospital, Sichuan University, Chengdu, China. [7]Department of Radiology, Seoul National University Hospital and Seoul National University College of Medicine, Seoul, South Korea. [8]Department of Radiology, Duke University Medical Center, Durham, North Carolina, USA. [9]Center for Advanced Magnetic Resonance in Medicine, Duke University Medical Center, Durham, North Carolina, USA. [10]Division of Gastroenterology, Department of Medicine, Duke University Medical Center, Durham, North Carolina, USA. [11]Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Medicine, Beihang University, Beijing, China. [12]Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education, School of Life Science and Technology, Xidian University, Xi'an, China. [13]Key Laboratory of Big Data-Based Precision Medicine (Beihang University), Ministry of Industry and Information Technology, Beijing, China.
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Microvascular invasion (MVI) is an important risk factor in hepatocellular carcinoma (HCC), but its diagnosis mandates postoperative histopathologic analysis. We aimed to develop and externally validate a predictive scoring system for MVI.From July 2015 to November 2020, consecutive patients underwent surgery for HCC with preoperative gadoxetate disodium (EOB)-enhanced MRI was retrospectively enrolled. All MR images were reviewed independently by two radiologists who were blinded to the outcomes. In the training centre, a radio-clinical MVI score was developed via logistic regression analysis against pathology. In the testing centre, areas under the receiver operating curve (AUCs) of the MVI score and other previous MVI schemes were compared. Overall survival (OS) and recurrence-free survival (RFS) were analysed by the Kaplan-Meier method with the log-rank test.A total of 417 patients were included, 195 (47%) with pathologically-confirmed MVI. The MVI score included: non-smooth tumour margin (odds ratio [OR] = 4.4), marked diffusion restriction (OR = 3.0), internal artery (OR = 3.0), hepatobiliary phase peritumoral hypointensity (OR = 2.5), tumour multifocality (OR = 1.6), and serum alpha-fetoprotein >400 ng/mL (OR = 2.5). AUCs for the MVI score were 0.879 (training) and 0.800 (testing), significantly higher than those for other MVI schemes (testing AUCs: 0.648-0.684). Patients with model-predicted MVI had significantly shorter OS (median 61.0 months vs not reached, P < .001) and RFS (median 13.0 months vs. 42.0 months, P < .001) than those without.A preoperative MVI score integrating five EOB-MRI features and serum alpha-fetoprotein level could accurately predict MVI and postoperative survival in HCC. Therefore, this score may aid in individualized treatment decision making.© 2022 The Authors. Liver International published by John Wiley & Sons Ltd.

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出版当年[2022]版:
大类 | 2 区 医学
小类 | 2 区 胃肠肝病学
最新[2023]版:
大类 | 2 区 医学
小类 | 2 区 胃肠肝病学
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第一作者机构: [1]Department of Radiology, West China Hospital, Sichuan University, Chengdu, Sichuan, China.
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通讯机构: [1]Department of Radiology, West China Hospital, Sichuan University, Chengdu, Sichuan, China. [2]Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China. [3]Beijing Key Laboratory of Molecular Imaging, Beijing, China. [4]Department of Medical Imaging, Henan Provincial People's Hospital, Zhengzhou, China. [5]Department of Medical Imaging, People's Hospital of Zhengzhou University, Zhengzhou, China. [11]Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Medicine, Beihang University, Beijing, China. [12]Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education, School of Life Science and Technology, Xidian University, Xi'an, China. [13]Key Laboratory of Big Data-Based Precision Medicine (Beihang University), Ministry of Industry and Information Technology, Beijing, China. [*1]Department of Medical Imaging, Henan Provincial People’s Hospital, Zhengzhou, Henan, 450003, China. [*2]Department of Radiology, West China Hospital, Sichuan University, Chengdu, Sichuan, 610041, China. [*3]Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.
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