Real-Time Cancer Progression Prediction using Temporal Graph Networks: Integrating Spatial and Temporal Dynamics for Enhanced Clinical Insights

Cancer progression varies significantly between individuals, making it crucial to develop models that not only detect early-stage cancer but also monitor its growth and progression over time. This study proposes a Temporal Graph Network (TGN)-based model for real-time cancer progression prediction, which integrates spatial and temporal patterns from cancerous cell data. Utilizing the "TCGA Lung Cancer Dataset" (The Cancer Genome Atlas Lung Adenocarcinoma and Lung Squamous Cell Carcinoma) and histopathological imaging data, our model builds dynamic graphs to capture the evolving structure and behavior of cancerous cells. The TGN framework is designed to model spatial dependencies in cancer cells, such as the biological interactions between genes and proteins, while also tracking temporal changes such as cell growth, mutations, and metastasis over time. The existing method is computationally expensive due to the complexity of graph convolutions combined with recurrent layers. The proposed TGN model addresses this issue by utilizing more efficient temporal and graph learning mechanisms that reduce the need for large-scale computations, making real-time predictions feasible. This novel approach offers real-time insights into cancer progression, allowing clinicians to forecast tumor growth trajectories, thereby enabling early and personalized interventions. The system can predict not only the presence of early-stage cancer but also future stages, offering a dynamic and individualized view of cancer progression that is essential for targeted treatments and monitoring aggressive cancers.