The field of medical imaging and survival prediction is rapidly evolving, with a growing focus on multimodal approaches that integrate diverse data sources to improve predictive accuracy and clinical decision-making. Recent studies have demonstrated the effectiveness of deep learning architectures in modeling complex relationships between medical images, clinical variables, and genomic data. These approaches have shown promise in enhancing survival prediction, tumor motion forecasting, and recurrence prediction in various cancer types. Noteworthy papers in this area include those that propose novel frameworks for multimodal feature fusion, such as cross-modality masked learning and parametric multimodal variational autoencoders. Additionally, the application of vision transformers to markerless tumor motion forecasting and the development of multimodal deep learning frameworks for precise recurrence prediction in clear cell renal cell carcinoma are also noteworthy. These innovative approaches have the potential to significantly advance the field and improve patient outcomes.