Survival Outcome

Survival outcome prediction in cancer research aims to accurately forecast patient survival time, aiding personalized treatment and improving clinical decision-making. Current research heavily utilizes deep learning models, including graph neural networks, residual networks, and transformers, often incorporating multimodal data (e.g., genomics, imaging) to enhance predictive accuracy. These advanced techniques are applied across various cancer types, focusing on improving model interpretability and addressing challenges like data heterogeneity and class imbalance. Ultimately, more accurate survival prediction models promise to significantly refine cancer care by enabling earlier interventions and more effective treatment strategies.