Journal of Shanghai Jiao Tong University (Medical Science) >

2025 , Vol. 45 >Issue 9: 1221 - 1231

DOI: https://doi.org/10.3969/j.issn.1674-8115.2025.09.014

Techniques and methods

Graph neural network-based auxiliary diagnostic model for gallbladder cancer on CT imaging

  • YIN Ziming ,
  • WANG Rongqin ,
  • YANG Ziyi ,
  • LIU Yingbin ,
  • CHEN Tao ,
  • SHU Yijun ,
  • GONG Wei
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  • 1.School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
    2.Department of General Surgery, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200092, China
    3.Department of Hepatobiliary and Pancreatic Surgery, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200127, China
GONG Wei, E-mail: gongwei@xinhuamed.com.cn.

Received date: 2025-06-09

  Accepted date: 2025-08-25

  Online published: 2025-09-30

Supported by

National Natural Science Foundation of China(82173081);Young Talents in Health of Shanghai Municipal Health Commission(2022YQ002);“Two-hundred Talents” Program of Shanghai Jiao Tong University School of Medicine(20151001)

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Abstract

Objective ·To develop a graph neural network (GNN)-based auxiliary diagnostic model for gallbladder cancer on CT images, and validate its accuracy and feasibility. Methods ·From January 2010 to November 2023, 1 774 contrast-enhanced CT arterial-phase images were acquired from 887 patients with normal gallbladder, benign gallbladder disease, or gallbladder cancer at Xinhua Hospital and Renji Hospital, Shanghai Jiao Tong University School of Medicine. These images were randomly divided into training and testing sets at a 4∶1 ratio to develop a hybrid GNN-convolutional neural network (CNN) model, named VJK-GIN. The model constructed a pixel-level graph in which each pixel served as a node, and spatial adjacency defined the edges, enabling extraction of local texture features. In the model architecture design, VJK-GIN integrated a three-layer graph isomorphism network, augmented with virtual nodes and jump-knowledge connections; global pooling compressed node features into a graph-level representation, which was classified by a multi-layer perceptron head. Five-fold cross-validation was used to compare VJK-GIN with GNN baselines (GCN, GraphSAGE, GAT, and GIN) and CNN baselines (ViT, EfficientNetV2, and ConvNeXt) in terms of accuracy, precision, recall, F1-score, and area under the receiver operating characteristic curve (AUC). Results ·The results of five-fold cross-validation showed that VJK-GIN achieved an F1-score of 0.799 (95%CI 0.775‒0.823), recall of 0.795 (95%CI 0.773‒0.817), precision of 0.799 (95%CI 0.775‒0.823), AUC of 0.812 (95%CI 0.792‒0.832), and accuracy of 0.773 (95%CI 0.748‒0.798), surpassing all competing models across every metric. Conclusion ·The VJK-GIN model exhibits high stability and accuracy in identifying contrast-enhanced CT images of normal, benign, and malignant gallbladder conditions.

Key words： gallbladder cancer; graph neural network (GNN); convolutional neural network (CNN); medical image analysis; deep learning

Cite this article

YIN Ziming , WANG Rongqin , YANG Ziyi , LIU Yingbin , CHEN Tao , SHU Yijun , GONG Wei . Graph neural network-based auxiliary diagnostic model for gallbladder cancer on CT imaging[J]. Journal of Shanghai Jiao Tong University (Medical Science), 2025 , 45(9) : 1221 -1231 . DOI: 10.3969/j.issn.1674-8115.2025.09.014

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