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

doi:10.3969/j.issn.1674-8115.2025.09.014

Abstract

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

CLC Number:

R735.8

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.

Figures/Tables 7

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References 32

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Item	Total	Training set	Testing set	P value
Number of patients/n	887	709	178	‒
Number of CT slices/n	1 774	1419	355	‒
Male/n (%)	447 (50.4)	360 (50.8)	87 (49.2)	0.712
Age/year	64.6±10.4	65.0±10.6	63.0±9.6	0.331
BMI/(kg·m^-2)	24.3±3.1	24.4±3.5	23.9±0.18	0.383
Disease type/n (%)				>0.999
Cancer	266 (30)	213 (30)	53 (30)
Benign	266 (30)	213 (30)	53 (30)
Normal	355 (40)	283 (40)	72 (40)

Item	Total	Training set	Testing set	P value
Number of patients/n	887	709	178	‒
Number of CT slices/n	1 774	1419	355	‒
Male/n (%)	447 (50.4)	360 (50.8)	87 (49.2)	0.712
Age/year	64.6±10.4	65.0±10.6	63.0±9.6	0.331
BMI/(kg·m^-2)	24.3±3.1	24.4±3.5	23.9±0.18	0.383
Disease type/n (%)				>0.999
Cancer	266 (30)	213 (30)	53 (30)
Benign	266 (30)	213 (30)	53 (30)
Normal	355 (40)	283 (40)	72 (40)