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• W2008017166 abstract "The imaging (e.g. CT/MRI slices including the definition of the tumor contour), the histologic and the genetic data of the patient are appropriately collected. Labels are assigned to the tumor and the anatomical structures of interest. The output of this procedure is introduced into the 3-D visualization package AVS-Express, which performs the visualization of the region of interest. Subsequently, a 3-D discretizing mesh is superimposed on the same region. Each geometrical cell of the mesh belonging to the tumor contains a number of biological cells “ residing”in various phases within or out of the cell cycle. Within each geometrical cell, a number of classes of biological cells, each one characterized by the phase in which its cells reside at any given instant, are de fined. Sufficient registers are used in order to characterize the state of each geometrical cell and each phase class within it (e.g. the number of biological cells in phase G1). The number of biological cells constituting each phase class is initially estimated according to the position of the geometrical cell within the tumor, the expected vasculature of the geometrical cell and consequently the oxygen and nutrient supply, the metabolic activity in the local area (e.g.based on PET/functional MRI) etc. A simplifying assumption dictates that each geometrical cell of the mesh can ’ normally’accommodate a constant Number of Biological Cells (NBC). In case that the actual number of living and dead (but still morphologically existing) tumor cells contained within a given geometrical cell is reduced to less than NBC/2, the entire geometrical cell is assumed to either disappear from the tumor or to constitute part of its inner necrosed region. As a consequence, the whole tumor shrinks. Tumor expansion is simulated in the opposite way. The geometrical mesh covering the anatomic area of interest is scanned every T units of time. For each phase class of a given geometrical cell, behavior algorithms based on the cell cycle phase durations of the tumor cells, the estimated local blood supply, the radiation survival curves of the tumor cells, the genetic data of the tumor (e.g. wild or mutated p53, bcl-2 genes) etc. determine the updated state. For simplification purposes all biological cells constituting each phase class within the same geometrical cell are assumed to be synchronized. An important aspect of the software is the virtual reality visualization of the anatomical region of interest, before, during and after the treatment course. Such a tool would enhance the doctor’ s spatio-temporal perception of the tumor behavior. The three dimensional progress of a tumor is simulated, initially without the application of any therapeutic schemes. In the case of radiotherapy and at the end of the physical treatment planning procedure, the distribution of the absorbed dose (e.g. in Gy) in the region of interest is provided. This distribution is used in order to “ predict”the most likely spatio-temporal response of the tumor. At the same time, the response of the adjacent irradiated normal tissues is simulated. The visualization procedure described in the beginning of this abstract is followed here too. The software system under development is currently undergoing an extensive validation and optimization procedure based on pertinent clinical data concerning e.g. astrocytoma irradiation." @default.
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• W2008017166 date "2001-11-01" @default.
• W2008017166 modified "2023-09-23" @default.
• W2008017166 title "In vivo tumor growth and response to radiation therapy: a novel algorithmic description" @default.
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• W2008017166 doi "https://doi.org/10.1016/s0360-3016(01)02261-1" @default.
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