The recent advancements in computational pathology and multimodal data integration are significantly enhancing diagnostic precision and efficiency. A notable trend is the development of frameworks that leverage multimodal data, such as combining whole slide images (WSIs) with genomic data, to improve classification and survival prediction tasks. These approaches often employ advanced fusion techniques, such as dual fusion strategies, to capture complementary information from different data modalities. Additionally, there is a growing emphasis on integrating domain-specific knowledge, such as pathology foundation models and language-guided mechanisms, to enhance the interpretability and accuracy of diagnostic models. Another emerging area is the use of hierarchical and selective aggregation methods in transformer-based models to efficiently process large-scale WSIs, reducing computational load while maintaining high diagnostic performance. Furthermore, the field is witnessing innovations in open-ended visual question answering and spatial gene expression prediction, which aim to bridge the gap between histopathology images and molecular insights, thereby advancing precision oncology. These developments collectively underscore the transformative potential of deep learning and multimodal integration in improving cancer diagnosis and treatment planning.