CT-based radiomics to predict development of macrovascular invasion in hepatocellular carcinoma: A multicenter study
文献类型:期刊论文
作者 | Jingwei Wei7; Sirui Fu6; Jie Zhang5; Dongsheng Gu7; Xiaoqun Li4; Xudong Chen3; Shuaitong Zhang7; Xiaofei He1; Jianfeng Yan2; Ligong Lu6 |
刊名 | Hepatobiliary & Pancreatic Diseases International |
出版日期 | 2021 |
卷号 | 2021期号:--页码:-- |
ISSN号 | 1499-3872 |
关键词 | Computed tomography Hepatocellular carcinoma Macrovascular invasion Prognosis Radiomics |
DOI | 10.1016/j.hbpd.2021.09.011 |
文献子类 | article |
英文摘要 | BACKGROUND: Macrovascular invasion (MaVI) occurs in nearly half of hepatocellular carcinoma (HCC) patients 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. |
URL标识 | 查看原文 |
语种 | 英语 |
源URL | [http://ir.ia.ac.cn/handle/173211/47447] |
专题 | 自动化研究所_中国科学院分子影像重点实验室 |
作者单位 | 1.Interventional Diagnosis and Treatment Department, Nanfang Hospital 2.Department of Radiology, Yangjiang People's Hospital 3.Department of Radiology, Shenzhen People's Hospital 4.Department of Interventional Treatment, Zhongshan City People's Hospital 5.Department of Radiology, Zhuhai Precision Medical Center, Zhuhai People's Hospital 6.Zhuhai Interventional Medical Center, Zhuhai Precision Medical Center, Zhuhai People's Hospital 7.Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences |
推荐引用方式 GB/T 7714 | Jingwei Wei,Sirui Fu,Jie Zhang,et al. CT-based radiomics to predict development of macrovascular invasion in hepatocellular carcinoma: A multicenter study[J]. Hepatobiliary & Pancreatic Diseases International,2021,2021(--):--. |
APA | Jingwei Wei.,Sirui Fu.,Jie Zhang.,Dongsheng Gu.,Xiaoqun Li.,...&Jie Tian.(2021).CT-based radiomics to predict development of macrovascular invasion in hepatocellular carcinoma: A multicenter study.Hepatobiliary & Pancreatic Diseases International,2021(--),--. |
MLA | Jingwei Wei,et al."CT-based radiomics to predict development of macrovascular invasion in hepatocellular carcinoma: A multicenter study".Hepatobiliary & Pancreatic Diseases International 2021.--(2021):--. |
入库方式: OAI收割
来源:自动化研究所
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