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Development and validation of a radiomics-based method for macrovascular invasion prediction in hepatocellular carcinoma with prognostic implication
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Paper 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.

Paper Details

Date Published: 13 March 2019
PDF: 6 pages
Proc. SPIE 10950, Medical Imaging 2019: Computer-Aided Diagnosis, 109501N (13 March 2019); doi: 10.1117/12.2510932
Show Author Affiliations
Jingwei Wei, Institute of Automation (China)
Beijing Key Lab. of Molecular Imaging (China)
Univ. of Chinese Academy of Sciences (China)
Sirui Fu, Zhuhai People’s Hospital (China)
Shauitong Zhang, Institute of Automation (China)
Beijing Key Lab. of Molecular Imaging (China)
Univ. of Chinese Academy of Sciences (China)
Jie Zhang, Zhuhai People’s Hospital (China)
Dongsheng Gu, Institute of Automation (China)
Beijing Key Lab. of Molecular Imaging (China)
Univ. of Chinese Academy of Sciences (China)
Xiaoqun Li, Zhongshan City People's Hospital (China)
Xudong Chen, Shenzhen People’s Hospital (China)
Xiaofeng He, Nanfang Hospital, Southern Medical Univ. (China)
Jianfeng Yan, Yangjiang People’s Hospital (China)
Ligong Lu, Zhuhai People's Hospital (China)
Jie Tian, Institute of Automation (China)
Beijing Key Lab. of Molecular Medicine (China)
Univ. of Chinese Academy of Sciences (China)


Published in SPIE Proceedings Vol. 10950:
Medical Imaging 2019: Computer-Aided Diagnosis
Kensaku Mori; Horst K. Hahn, Editor(s)

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