Immunotherapy has become a fourth pillar in the treatment of brain tumors and, when combined with radiation therapy, may improve patient outcomes and reduce the neurotoxicity. We hope that this contribution provides motivation for the imaging science, oncology, and computational communities to develop practical digital twin technologies to improve the care of patients battling cancer. We conclude by outlining five fundamental questions that can serve as a roadmap when designing and building a practical digital twin for oncology and attempt to provide answers for a specific application to brain cancer. We then describe the current challenges and limitations in developing image-guided, mechanism-based digital twins for oncology along with potential solutions. Next, we detail both the imaging and modeling techniques that provide practical opportunities to build patient-specific digital twins for oncology. Specifically, we first introduce the general digital twin framework and then illustrate existing applications of image-guided digital twins in healthcare. In this review, we present the opportunities and challenges of applying digital twins in clinical oncology, with a particular focus on integrating medical imaging with mechanism-based, tissue-scale mathematical modeling. Due to advances in experimental techniques quantitatively characterizing cancer, as well as advances in the mathematical and computational sciences, the notion of building and applying digital twins to understand tumor dynamics and personalize the care of cancer patients has been increasingly appreciated. While digital twins have been widely used in engineering for decades, their applications to oncology are only just emerging. The cover art for this Roadmap was chosen as an apt metaphor for the beautiful, strange, and evolving relationship between mathematics and cancer.ĭigital twins employ mathematical and computational models to virtually represent a physical object (e.g., planes and human organs), predict the behavior of the object, and enable decision-making to optimize the future behavior of the object. This is achieved through the use of patient-specific clinical data to: develop individualized screening strategies to detect cancer earlier make predictions of response to therapy design adaptive, patient-specific treatment plans to overcome therapy resistance and establish domain-specific standards to share model predictions and to make models and simulations reproducible. The dominant theme of this Roadmap is the personalization of medicine through mathematics, modelling, and simulation. This Roadmap differentiates Mathematical Oncology from related fields and demonstrates specific areas of focus within this unique field of research. As a result, Mathematical Oncology has a broad scope, ranging from theoretical studies to clinical trials designed with mathematical models. Mathematical Oncology-defined here simply as the use of mathematics in cancer research-complements and overlaps with a number of other fields that rely on mathematics as a core methodology. Whether the nom de guerre is Mathematical Oncology, Computational or Systems Biology, Theoretical Biology, Evolutionary Oncology, Bioinformatics, or simply Basic Science, there is no denying that mathematics continues to play an increasingly prominent role in cancer research.
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