Prior Knowledge-Aware Fusion Network for Prediction of Macrovascular Invasion in Hepatocellular Carcinoma
Abstract:
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.
Insights
Predicting macrovascular invasion (MaVI) in hepatocellular carcinoma (HCC) is crucial for patient survival. A novel prior knowledge-aware fusion network (PKAFnet) effectively improves MaVI prediction accuracy using CT images.
Area of Science:
- Medical Imaging
- Artificial Intelligence
- Oncology
Background:
- Macrovascular invasion (MaVI) significantly impacts survival in hepatocellular carcinoma (HCC).
- Accurate prediction of MaVI in computed tomography (CT) images is challenging due to image variations and lack of clinical prior knowledge integration.
- Existing methods often fail to comprehensively extract information for effective MaVI prediction.
Purpose of the Study:
- To develop an accurate method for predicting macrovascular invasion (MaVI) in hepatocellular carcinoma (HCC) using CT images.
- To address the limitations of existing methods by incorporating clinical prior knowledge and multi-domain feature fusion.
- To enhance the discriminative ability and reduce overfitting in MaVI prediction.
Main Methods:
- Proposed a prior knowledge-aware fusion network (PKAFnet) integrating a perception module for graph-domain feature extraction (tumor marginal heterogeneity) and a segmentation network for image-domain feature extraction (internal heterogeneity).
- Employed a multi-domain attentional feature fusion module to combine features from the tumor margin and tumor region.
- Validated the PKAFnet on a multi-center dataset, assessing its performance on an independent testing set.
Main Results:
- The proposed PKAFnet demonstrated remarkable performance in MaVI prediction on an independent testing set.
- Incorporating MaVI-related prior knowledge effectively alleviated overfitting and improved the model's discriminative ability.
- Interpretability analysis of the network components confirmed the effectiveness and credibility of the PKAFnet.
Conclusions:
- The developed PKAFnet shows significant potential for accurate macrovascular invasion prediction in hepatocellular carcinoma.
- The integration of prior knowledge and multi-domain feature fusion offers a promising approach for improving diagnostic accuracy in medical imaging.
- The method's validated performance and interpretability highlight its clinical applicability for early MaVI detection and patient management.
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