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Predicting lung tumor evolution during radiotherapy from PET images using a patient specific model

Su Ruan
LITIS - Rouen University
JPG - 22.2 ko

We propose a patient-specific model based on PDE to predict the evolution of lung tumors during radiotherapy. The evolution of tumor cell densities is formulated by three terms : 1) advection describing the mobility, 2) reaction representing the proliferation modeled as Gompertz differential equation, and 3) treatment quantifying the radiotherapeutic efficacy modeled as exponential function. As tumor cell density variation can be derived from PET images, the novel idea is to model the advection term by calculating 3D optical flow field from sequential images.To estimate patient-specific parameters, we carry out an optimization between the predicted and observed images, under a volume-dose model constraint. We present the results obtained in 8 patients, where the predicted tumor contours are compared to those drawn by an expert.

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