Multimodal analysis and the oncology patient: Creating a hospital system for integrated diagnostics and discovery.

We propose that an information technology and computational framework that would unify access to hospital digital information silos, and enable integration of this information using machine learning methods, would bring a new paradigm to patient management and research. This is the core principle of Integrated Diagnostics (ID): the amalgamation of multiple analytical modalities, with evolved information technology, applied to a defined patient cohort, and resulting in a synergistic effect in the clinical value of the individual diagnostic tools . This has the potential to transform the practice of personalized oncology at a time at which it is very much needed. In this article we present different models from the literature that contribute to the vision of ID and we provide published exemplars of ID tools. We briefly describe ongoing efforts within a universal healthcare system to create national clinical datasets. Following this, we argue the case to create "hospital units" to leverage this multi-modal analysis, data integration and holistic clinical decision-making. Finally, we describe the joint model created in our institutions.
Competing Interests: CM receives funding from the Royal Marsden Cancer Charity and NIHR Biomedical Research Centre at The Royal Marsden and Institute of Cancer Research. Richard Lee reports funding indirectly related to this work including Royal Marsden Cancer charity, CRUK, Innovate UK (co-funded by Roche), RM Partners Cancer Alliance, NIHR, NHS England, and personal private practice. MST is a scientific advisor to Mindpeak and Sonrai Analytics, and has received honoraria recently from BMS, Roche, MSD and Incyte. None of these disclosures are related to this work.
(© 2023 The Authors. Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology.)