Prior Knowledge-Aware Fusion Network for Prediction of Macrovascular Invasion in Hepatocellular Carcinoma

• Published in: IEEE transactions on medical imaging 2022-10, Vol.41 (10), p.2644-2657
• Main Authors: Lai, Haoran, Fu, Sirui, Zhang, Jie, Cao, Jianyun, Feng, Qianjin, Lu, Ligong, Huang, Meiyan
• Format: Article
• Language: English
• Subjects: Computed tomography; Data mining; Domains; Feature extraction; Hepatocellular carcinoma; Heterogeneity; Hospitals; Image segmentation; Information retrieval; Lesions; Liver cancer; macrovascular invasion prediction; Medical imaging; Modules; multi-domain fusion; Perception; Predictions; prior knowledge; Radiomics; rotation invariance; Tumors
• ISSN: 0278-0062; 1558-254X
• DOI: 10.1109/TMI.2022.3167788

Macrovascular invasion (MaVI) is a major threat to survival in hepatocellular carcinoma (HCC), which should be treated as early as possible to ensure safety and efficacy. In this aspect, MaVI prediction can be helpful. However, MaVI prediction is difficult because of the inter-class similarity and intra-class variation of HCC in computed tomography (CT) images. Moreover, existing methods fail to include clinical priori knowledge associated with HCC, leading to incomprehensive information extraction. In this paper, we proposed a prior knowledge-aware fusion network (PKAFnet) to accurately achieve MaVI prediction in CT images. First, a perception module was presented to extract features related to tumor marginal heterogeneity in the graph domain, which contributed to rotation invariance and captured intensity variations of tumor margin. Second, a tumor segmentation network was built to obtain global information of a 3D tumor image and information associated with tumor internal heterogeneity in the image domain. Finally, multi-domain features associated with the tumor margin and tumor region were combined by using a multi-domain attentional feature fusion module. Thus, by incorporating MaVI-related prior knowledge, our PKAFnet can alleviate overfitting, which can improve the discriminative ability. The proposed PKAFnet was validated on a multi-center dataset, and remarkable performance was achieved in an independent testing set. Moreover, the interpretability of perception module and segmentation network were presented in our paper, which illustrated the effectiveness and credibility of PKAFnet. Therefore, the proposed method showed great application potential for MaVI prediction.