UBC Undergraduate Research
Linking radiomic PET features with metabolic tissue parameters using a hybrid mathematical model of tumor growth Ahn, Hailey S. H.
Tumor heterogeneity can be observed between and within tumors through medical imaging such as positron emission tomography (PET). Heterogeneity arises due to the genetic diversity in cancer cell populations and the dynamic microenvironments. Understanding the relationship between tumor tissue microparameters and quantitative PET radiomic features can offer a better strategy for caner diagnosis and treatment. Our goal was to develop a multiscale mathematical model for realistic tumor growth in vascularized tissue, and to generate synthetic PET images from the simulated images to study this relationship. The hybrid mathematical model used in the simulation combines an agent grid and partial differential equations to model the dynamic tumor microenvironments. The status of the cell and its behaviour is determined by the local concentration of oxygen and glucose which diffuse from the vessels to tissue. The simulated cell maps were converted to synthetic PET images by translating the spatial locations of the cells to the corresponding pseudo-standardized tracer uptake values of the PET tracer 18F-fluorodeoxyglucose, which are unique to each cell type. Using different combinations of tissue microparameters, we were able to generate tumors with distinct phenotypic profiles that were visually distinguishable in the translated synthetic PET images. Four radiomic features were computed from the resulting images and this demonstrated that unique tumor phenotypes can be linked to radiomic PET features. Moreover, the identified optimal radiomic features can be used as biomarkers for tumor assessment.
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