Cell graph neural networks enable the precise prediction of patient survival in gastric cancer

Gastric cancer is one of the deadliest cancers worldwide. An accurate prognosis is essential for effective clinical assessment and treatment. Spatial patterns in the tumor microenvironment (TME) are conceptually indicative of the staging and progression of gastric cancer patients. Using spatial patterns of the TME by integrating and transforming the multiplexed immunohistochemistry (mIHC) images as Cell-Graphs, we propose a graph neural network-based approach, termed Cell-Graph Signature or CG(signature), powered by artificial intelligence, for the digital staging of TME and precise prediction of patient survival in gastric cancer. In this study, patient survival prediction is formulated as either a binary (short-term and long-term) or ternary (short-term, medium-term, and long-term) classification task. Extensive benchmarking experiments demonstrate that the CG(signature) achieves outstanding model performance, with Area Under the Receiver Operating Characteristic curve of 0.960 +/- 0.01, and 0.771 +/- 0.024 to 0.904 +/- 0.012 for the binary- and ternary-classification, respectively. Moreover, Kaplan-Meier survival analysis indicates that the digital grade cancer staging produced by CG(signature )provides a remarkable capability in discriminating both binary and ternary classes with statistical significance (P value < 0.0001), significantly outperforming the AJCC 8th edition Tumor Node Metastasis staging system. Using Cell-Graphs extracted from mIHC images, CG(signature) improves the assessment of the link between the TME spatial patterns and patient prognosis. Our study suggests the feasibility and benefits of such an artificial intelligence-powered digital staging system in diagnostic pathology and precision oncology.

Automated summary

This study proposes an AI-powered digital staging system that analyzes spatial patterns in the tumor microenvironment to accurately predict survival rates and staging of gastric cancer patients. The results show outstanding model performance and significant improvement over traditional staging systems.