Cancer Survival Prediction

Cancer survival prediction aims to accurately estimate a patient's survival time after diagnosis, guiding personalized treatment strategies. Current research heavily focuses on leveraging multimodal data, such as genomic information and histopathological images, integrated through advanced machine learning models including deep learning architectures like transformers, autoencoders, and multiple instance learning frameworks. These models are being refined to improve prediction accuracy, enhance interpretability, and address challenges like data heterogeneity and high dimensionality. Ultimately, improved prediction accuracy holds significant potential to optimize cancer care by enabling more precise risk stratification and treatment planning.