PMID- 35436183
OWN - NLM
STAT- MEDLINE
DCOM- 20221004
LR  - 20221012
IS  - 1558-254X (Electronic)
IS  - 0278-0062 (Linking)
VI  - 41
IP  - 10
DP  - 2022 Oct
TI  - Prior Knowledge-Aware Fusion Network for Prediction of Macrovascular Invasion in 
      Hepatocellular Carcinoma.
PG  - 2644-2657
LID - 10.1109/TMI.2022.3167788 [doi]
AB  - 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.
FAU - Lai, Haoran
AU  - Lai H
FAU - Fu, Sirui
AU  - Fu S
FAU - Zhang, Jie
AU  - Zhang J
FAU - Cao, Jianyun
AU  - Cao J
FAU - Feng, Qianjin
AU  - Feng Q
FAU - Lu, Ligong
AU  - Lu L
FAU - Huang, Meiyan
AU  - Huang M
LA  - eng
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
DEP - 20220930
PL  - United States
TA  - IEEE Trans Med Imaging
JT  - IEEE transactions on medical imaging
JID - 8310780
SB  - IM
MH  - *Carcinoma, Hepatocellular/diagnostic imaging/pathology
MH  - Humans
MH  - Image Processing, Computer-Assisted/methods
MH  - Imaging, Three-Dimensional
MH  - *Liver Neoplasms/diagnostic imaging
MH  - Neoplastic Processes
MH  - Tomography, X-Ray Computed/methods
EDAT- 2022/04/19 06:00
MHDA- 2022/10/05 06:00
CRDT- 2022/04/18 17:10
PHST- 2022/04/19 06:00 [pubmed]
PHST- 2022/10/05 06:00 [medline]
PHST- 2022/04/18 17:10 [entrez]
AID - 10.1109/TMI.2022.3167788 [doi]
PST - ppublish
SO  - IEEE Trans Med Imaging. 2022 Oct;41(10):2644-2657. doi: 10.1109/TMI.2022.3167788. 
      Epub 2022 Sep 30.