Discrimination of macrotrabecular-massive hepatocellular carcinoma based on fusion of multi-phase contrast-enhanced computed tomography radiomics features_Journal of Biomedical Engineering

Authors：

ZHANG Zhenyang ^1,2 , XIE Jincheng ² , ZHONG Weixiong ² , LIANG Fangrong ³ , ZHANG Wanli ³ , SUN Yan ⁴ ,  ZHEN Xin ^2,5 ,  YANG Ruimeng ^1,3

1. Department of Radiology, the Second Affiliated Hospital, School of Medicine, South China University of Technology, Guangzhou 510180, P. R. China;
2. School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, P. R. China;
3. School of Medicine, South China University of Technology, Guangzhou 510006, P. R. China;
4. School of Life Sciences, Nankai University, Tianjin 300350, P. R. China;
5. Medical Big Data Center, Guangdong Provincial People’s Hospital, Southern Medical University, Guangzhou 510080, P. R. China;

Corresponding?author：

ZHEN Xin, Email: xinzhen@smu.edu.cn; YANG Ruimeng, Email: eyruimengyang@scut.edu.cn

Keywords：

Macrotrabecular-massive; Hepatocellular carcinoma; Multi-phase contrast-enhanced computed tomography; Radiomics; Feature fusion

DOI：

10.7507/1001-5515.202401031

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The macrotrabecular-massive (MTM) subtype of hepatocellular carcinoma (HCC) is a histological variant with higher malignant potential. Non-invasive preoperative identification of MTM-HCC is crucial for precise treatment. Current radiomics-based diagnostic models often integrate multi-phase features by simple feature concatenation, which may inadequately explore the latent complementary information between phases. This study proposes a feature fusion-based radiomics model using multi-phase contrast-enhanced computed tomography (mpCECT) images. Features were extracted from the arterial phase (AP), portal venous phase (PVP), and delayed phase (DP) CT images of 121 HCC patients. The fusion model was constructed and compared against the traditional concatenation model. Five-fold cross-validation demonstrated that the feature fusion model combining AP and PVP features achieved the best classification performance, with an area under the receiver operating characteristic curve (AUC) of 0.839. Furthermore, for any combination of two phases, the feature fusion model consistently outperformed the traditional feature concatenation approach. In conclusion, the proposed feature fusion model effectively enhances the discrimination capability compared to traditional models, providing a new tool for clinical practice.

Citation： ZHANG Zhenyang, XIE Jincheng, ZHONG Weixiong, LIANG Fangrong, ZHANG Wanli, SUN Yan, ZHEN Xin, YANG Ruimeng. Discrimination of macrotrabecular-massive hepatocellular carcinoma based on fusion of multi-phase contrast-enhanced computed tomography radiomics features. Journal of Biomedical Engineering, 2025, 42(6): 1198-1204. doi: 10.7507/1001-5515.202401031 Copy

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Journal of Biomedical Engineering

Discrimination of macrotrabecular-massive hepatocellular carcinoma based on fusion of multi-phase contrast-enhanced computed tomography radiomics features

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