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Showing 1 to 6 of 6 for “"mathematical oncology"”.
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Combining mechanistic modelling with data-driven modelling in mathematical oncology
Mathematical modelling in cancer research has been developing and has contributed valuable insights. By helping to understand the mechanisms of biological processes and predict clinical outcomes, mathematical models can complement empirical cancer research. In chapter 2, we studied the dynamics by …
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From Oncogenesis to Immunotherapy: Mathematical Modeling of Heterogeneous Cancers
We utilize mathematical modeling to study two types of cancer with substantial mutational heterogeneity: chronic lymphocytic leukemia (CLL) and mismatch-repair deficient colorectal cancer (MMR-D CRC). First, to study the progression of CLL into an aggressive lymphoma, Richter’s Syndrome (RS), we …
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A New Mathematical Theory for the Dynamics of Large Tumor Populations, a Potential Mechanism for Cancer Dormancy & Recurrence and Experimental Observation of Melanoma Progression in Zebrafish
… they necessary? In contrast, we propose a new mathematical model that predicts long-term apparent dormancy followed by recurrence can occur because of tumor population dynamics without the need for a single random event such as the occurrence of a key mutation. </p> <p> Understanding the …
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Mathematical approaches for the clinical translation of hyperpolarised 13C imaging in oncology
… into the processes driving lactate metabolism, a mathematical model is developed which is capable of simulating tumour growth and treatment response under a range of metabolic and tissue conditions, focusing on the interaction between tumour and stroma. Finally, hyperpolarised 13C-pyruvate imaging …
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Mathematical modelling of brain tumour growth. Integrating mechanics, phenotypic evolution, and nutrient transport to describe anisotropic behaviour and personalised therapies.
L'abstract è presente nell'allegato / the abstract is in the attachment