STG: Spatiotemporal Graph Neural Network with Fusion and Spatiotemporal Decoupling Learning for Prognostic Prediction of Colorectal Cancer Liver Metastasis
Journal:
arXiv
Published Date:
May 6, 2025
Abstract
We propose a multimodal spatiotemporal graph neural network (STG) framework
to predict colorectal cancer liver metastasis (CRLM) progression. Current
clinical models do not effectively integrate the tumor's spatial heterogeneity,
dynamic evolution, and complex multimodal data relationships, limiting their
predictive accuracy. Our STG framework combines preoperative CT imaging and
clinical data into a heterogeneous graph structure, enabling joint modeling of
tumor distribution and temporal evolution through spatial topology and
cross-modal edges. The framework uses GraphSAGE to aggregate spatiotemporal
neighborhood information and leverages supervised and contrastive learning
strategies to enhance the model's ability to capture temporal features and
improve robustness. A lightweight version of the model reduces parameter count
by 78.55%, maintaining near-state-of-the-art performance. The model jointly
optimizes recurrence risk regression and survival analysis tasks, with
contrastive loss improving feature representational discriminability and
cross-modal consistency. Experimental results on the MSKCC CRLM dataset show a
time-adjacent accuracy of 85% and a mean absolute error of 1.1005,
significantly outperforming existing methods. The innovative heterogeneous
graph construction and spatiotemporal decoupling mechanism effectively uncover
the associations between dynamic tumor microenvironment changes and prognosis,
providing reliable quantitative support for personalized treatment decisions.
