Keywords: In Silico, Quantitative Imaging, Digital twin
Motivation: Prediction of cancer therapy outcomes is a paramount objective in oncology, closely tied to the integration of novel biomarkers into clinical practice.
Goal(s): The goal of creating digital twins of solid tumors is to equip oncologists with a comprehensive replica of the tumor, allowing them to make well-informed decisions.
Approach: In the development of digital twins for solid tumors, we introduce a multidisciplinary approach that essentially combines quantitative MRI and computational modeling.
Results: The image-based model yields a comprehensive representation of tumor perfusion, providing a map of elevated interstitial fluid pressure, which holds significant potential as a biomarker in oncology.
Impact: Cancer therapy's success is not guaranteed, with potential serious side effects. Our aim is to offer a robust digital tumor replica for evaluating numerous treatment options, identifying the optimal plan while minimizing adverse effects.
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