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Bayesian Inference for Patient-Specific Digital Twins in Oncology
DescriptionPredictive digital twins are poised to make an impact in the bourgeoning field of precision oncology by coupling mathematical and computational models with patient-specific data. The inherent inter-patient heterogeneity in cancer physiology and response to therapy hinders development of therapies at the population level that are effective for individual patient. While it is not feasible to perform multiple in vivo trials on an individual patient, computational models augment the traditional approach by enabling in silico assessment of potential interventions. A digital twin deployed in the clinic would calibrate models of disease progression with patient-specific data, predict patient outcomes, and inform treatment strategies, thereby tailoring care to the individual. Realizing such a digital twin will require scalable and efficient methods to integrate patient data with computational models. We develop an end-to-end framework that combines longitudinal magnetic resonance imaging (MRI) with mechanistic models of disease progression.