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 state-of-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.

Despite the impressive performance of current deep learning models in the field of medical imaging, transferring the lung segmentation task in X-ray images to clinical practice is still a pending task. In this study, the performance of a fully automatic framework for lung field segmentation in chest X-ray images was evaluated. The framework is rooted in the combination of the Segment Anything Model (SAM) with prompt capabilities, and the You Only Look Once (YOLO) model to provide effective prompts. Transfer learning, loss functions, and several validation strategies were thoroughly assessed. This provided a complete benchmark that enabled future research studies to fairly compare new segmentation strategies. The results achieved demonstrated significant robustness and generalization capability against the variability in sensors, populations, disease manifestations, device processing, and imaging conditions. The proposed framework was computationally efficient, could address bias in training over multiple datasets, and had the potential to be applied across other domains and modalities.