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CT-based radiomics to predict development of macrovascular invasion in hepatocellular carcinoma: A multicenter study |
Jing-Wei Wei a , b , c , # , Si-Rui Fu d , # , Jie Zhang e , # , Dong-Sheng Gu a , b , c , # , Xiao-Qun Li f , Xu-Dong Chen g , Shuai-Tong Zhang a , b , c , Xiao-Fei He h , Jian-Feng Yan i , Li-Gong Lu d , ∗, Jie Tian a , b , c , j , k , ∗ |
a Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
b Beijing Key Laboratory of Molecular Imaging, Beijing 100190, China
c University of Chinese Academy of Sciences, Beijing 100049, China
d Zhuhai Interventional Medical Center, Zhuhai Precision Medical Center, Zhuhai People’s Hospital, Zhuhai Hospital of Jinan University, Zhuhai 519000, China
e Department of Radiology, Zhuhai Precision Medical Center, Zhuhai People’s Hospital, Zhuhai Hospital of Jinan University, Zhuhai 519000, China
f Department of Interventional Treatment, Zhongshan City People’s Hospital, Zhongshan 528400, China
g Department of Radiology, Shenzhen People’s Hospital, Shenzhen 518000, China
h Interventional Diagnosis and Treatment Department, Nanfang Hospital, Southern Medical University, Guangzhou, 510000, China
i Department of Radiology, Yangjiang People’s Hospital, Yangjiang 529500, China
j Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Medicine and Engineering, Beihang University, Beijing 100191, China
k Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education, School of Life Science and Technology, Xidian University, Xi’an 710126, China
∗ Corresponding authors: Li-Gong Lu, Zhuhai Interventional Medical Center, Zhuhai Precision Medical Center, Zhuhai People’s Hospital, Zhuhai Hospital of Jinan University, Zhuhai 519000, China
Jie Tian, Key Laboratory of Molecular Imaging of Chinese Academy of Sciences, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
E-mail addresses: llg0902@sina.com (L.-G. Lu), tian@ieee.org (J. Tian).
# Contributed equally. |
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Abstract Background: Macrovascular invasion (MaVI) occurs in nearly half of hepatocellular carcinoma (HCC) pa- tients at diagnosis or during follow-up, which causes severe disease deterioration, and limits the possibility of surgical approaches. This study aimed to investigate whether computed tomography (CT)-based radiomics analysis could help predict development of MaVI in HCC.
Methods: A cohort of 226 patients diagnosed with HCC was enrolled from 5 hospitals with complete MaVI and prognosis follow-ups. CT-based radiomics signature was built via multi-strategy machine learning methods. Afterwards, MaVI-related clinical factors and radiomics signature were integrated to construct the final prediction model (CRIM, clinical-radiomics integrated model) via random forest modeling. Cox-regression analysis was used to select independent risk factors to predict the time of MaVI development. Kaplan-Meier analysis was conducted to stratify patients according to the time of MaVI development, progression-free survival (PFS), and overall survival (OS) based on the selected risk factors.
Results: The radiomics signature showed significant improvement for MaVI prediction compared with conventional clinical/radiological predictors (P < 0.001). CRIM could predict MaVI with satisfactory areas under the curve (AUC) of 0.986 and 0.979 in the training (n = 154) and external validation (n = 72) datasets, respectively. CRIM presented with excellent generalization with AUC of 0.956, 1.000, and 1.000 in each external cohort that accepted disparate CT scanning protocol/manufactory. Peel9_fos_InterquartileRange [hazard ratio (HR) = 1.98; P < 0.001] was selected as the independent risk factor. The cox-regression model successfully stratified patients into the high-risk and low-risk groups regarding the time of MaVI development ( P < 0.001), PFS (P < 0.001) and OS (P = 0.002).
Conclusions: The CT-based quantitative radiomics analysis could enable high accuracy prediction of subsequent MaVI development in HCC with prognostic implications.
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