  Cancer has relational information residing at varying scales, modalities, and
resolutions of the acquired data, such as radiology, pathology, genomics,
proteomics, and clinical records. Integrating diverse data types can improve
the accuracy and reliability of cancer diagnosis and treatment. There can be
disease-related information that is too subtle for humans or existing
technological tools to discern visually. Traditional methods typically focus on
partial or unimodal information about biological systems at individual scales
and fail to encapsulate the complete spectrum of the heterogeneous nature of
data. Deep neural networks have facilitated the development of sophisticated
multimodal data fusion approaches that can extract and integrate relevant
information from multiple sources. Recent deep learning frameworks such as
Graph Neural Networks (GNNs) and Transformers have shown remarkable success in
multimodal learning. This review article provides an in-depth analysis of the
state-of-the-art in GNNs and Transformers for multimodal data fusion in
oncology settings, highlighting notable research studies and their findings. We
also discuss the foundations of multimodal learning, inherent challenges, and
opportunities for integrative learning in oncology. By examining the current
state and potential future developments of multimodal data integration in
oncology, we aim to demonstrate the promising role that multimodal neural
networks can play in cancer prevention, early detection, and treatment through
informed oncology practices in personalized settings.
