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

Jingwei Wei; Sirui Fu; Shauitong Zhang; Jie Zhang; Dongsheng Gu; Xiaoqun Li; Xudong Chen; Xiaofeng He; Jianfeng Yan; Ligong Lu; Jie Tian

doi:10.1117/12.2510932

13 March 2019 Development and validation of a radiomics-based method for macrovascular invasion prediction in hepatocellular carcinoma with prognostic implication

Jingwei Wei, Sirui Fu, Shauitong Zhang, Jie Zhang, Dongsheng Gu, Xiaoqun Li, Xudong Chen, Xiaofeng He, Jianfeng Yan, Ligong Lu, Jie Tian

Author Affiliations +

Proceedings Volume 10950, Medical Imaging 2019: Computer-Aided Diagnosis; 109501N (2019) https://doi.org/10.1117/12.2510932
Event: SPIE Medical Imaging, 2019, San Diego, California, United States

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.

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Jingwei Wei, Sirui Fu, Shauitong Zhang, Jie Zhang, Dongsheng Gu, Xiaoqun Li, Xudong Chen, Xiaofeng He, Jianfeng Yan, Ligong Lu, and Jie Tian "Development and validation of a radiomics-based method for macrovascular invasion prediction in hepatocellular carcinoma with prognostic implication", Proc. SPIE 10950, Medical Imaging 2019: Computer-Aided Diagnosis, 109501N (13 March 2019); https://doi.org/10.1117/12.2510932

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