CASIA OpenIR  > 中国科学院分子影像重点实验室
Development and validation of a radiomics-based method for macrovascular invasion prediction in hepatocellular carcinoma with prognostic implication
Wei Jingwei1,2,3; Fu Sirui4; Zhang Shuaitong1,2,3; Zhang Jie5; Gu Dongsheng1,2,3; Li Xiaoqun6; Chen Xudong7; He Xiaofeng8; Yan Jianfeng9; Lu Ligong4; Tian Jie1,2,3
2019-02
Conference NameSPIE 国际光学工程学会年会
Conference Date2019年2月20日
Conference Place美国圣地亚哥
Abstract

In hepatocellular carcinoma (HCC), more than one third of patients were accompanied by macrovascular invasion (MaVI) during diagnosis and treatment. HCCs with MaVI presented with aggressive tumor behavior and poor survival. Early identification of HCCs at high risk of MaVI would promote adequate preoperative treatment strategy making, so as to prolong the patient survival. Thus, we aimed to develop a computed tomography (CT)-based radiomics model to preoperatively predict MaVI status in HCC, meanwhile explore the prognostic prediction power of the radiomics model.

A cohort of 452 patients diagnosed with HCC was collected from 5 hospitals in China with complete CT images, clinical data, and follow-ups. 15 out of 708 radiomic features were selected for MaVI prediction using LASSO regression modeling. A radiomics signature was constructed by support vector machine based on the 15 selected features. To evaluate the prognostic power of the signature, Kaplan-Meier curves with log-rank test were plotted on MaVI occurrence time (MOT), progression free survival (PFS) and overall survival (OS).

The radiomics signature showed satisfactory performance on MaVI prediction with area under curves of 0.885 and 0.770 on the training and external validation cohorts, respectively. Patients could successfully be divided into high- and low-risk groups on MOT and PFS with p-value of 0.0017 and 0.0013, respectively. Regarding to OS, the Kaplan-Meier curve did not present with significant difference which may be caused by non-uniform following treatments after disease progression.

To conclude, the proposed radiomics model could facilitate MaVI prediction along with prognostic implication in HCC management.

Keywordhepatocellular carcinoma macrovascular invasion prognosis computed tomography radiomics prediction
Indexed ByEI
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/23677
Collection中国科学院分子影像重点实验室
Corresponding AuthorLu Ligong; Tian Jie
Affiliation1.Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
2.Beijing Key Laboratory of Molecular Imaging, Beijing 100190, China
3.University of Chinese Academy of Sciences, Beijing 100049, China
4.Center of Intervention Radiology, Center of Precise Medicine, Zhuhai People’s Hospital, Zhuhai, China
5.Department of Radiology, Center of Precise Medicine, Zhuhai People’s Hospital Zhuhai, China
6.Department of Interventional Treatment, Zhongshan City People's Hospital, Zhongshan, China
7.Department of Radiology, Shenzhen People’s Hospital, Shenzhen, China
8.Interventional Diagnosis and Treatment Department, Nanfang Hospital, Southern Medical University, Guangzhou, China
9.Department of Radiology, Yangjiang People’s hospital, Yangjiang, China
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Corresponding Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Wei Jingwei,Fu Sirui,Zhang Shuaitong,et al. Development and validation of a radiomics-based method for macrovascular invasion prediction in hepatocellular carcinoma with prognostic implication[C],2019.
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