Cancer is one of the most detrimental diseases globally. Accordingly, the prognosis
prediction of cancer patients has become a field of interest. In this review, we have gathered 43 stateof-
the-art scientific papers published in the last 6 years that built cancer prognosis predictive models
using multimodal data. We have defined the multimodality of data as four main types: clinical,
anatomopathological, molecular, and medical imaging; and we have expanded on the information
that each modality provides. The 43 studies were divided into three categories based on the modelling
approach taken, and their characteristics were further discussed together with current issues and
future trends. Research in this area has evolved from survival analysis through statistical modelling
using mainly clinical and anatomopathological data to the prediction of cancer prognosis through a
multi-faceted data-driven approach by the integration of complex, multimodal, and high-dimensional
data containing multi-omics and medical imaging information and by applying Machine Learning
and, more recently, Deep Learning techniques. This review concludes that cancer prognosis predictive
multimodal models are capable of better stratifying patients, which can improve clinical management
and contribute to the implementation of personalised medicine as well as provide new and valuable
knowledge on cancer biology and its progression
