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Some Models for the Prediction of Tumor Growth: General Framework and Applications to Metastases in the Lung

  • Thierry Colin
  • Angelo Iollo
  • Damiano Lombardi
  • Olivier SautEmail author
  • Françoise Bonichon
  • Jean Palussière
Chapter

Abstract

This chapter presents an example of an application of a mathematical model: the goal is here to help clinicians evaluate the aggressiveness of some metastases to the lung. For this matter, an adequate spatial model is described and two algorithms (one using a reduced model approach and the other one a sensitivity technique) are shown. They allow us to find reasonable values of the parameters of this model for a given patient with a sequence of medical images. The quality of the prognosis obtained through the calibrated model is then illustrated with several clinical cases.

Keywords

Tumor growth modeling Clinical data assimilation Inverse problem Partial differential equations Scientific computing Numerical prognosis lung cancer metastases reduce model genetic regulation carcinoma clinical decision 

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Copyright information

© Springer New York 2014

Authors and Affiliations

  • Thierry Colin
    • 1
  • Angelo Iollo
    • 1
  • Damiano Lombardi
    • 1
  • Olivier Saut
    • 1
    Email author
  • Françoise Bonichon
    • 2
  • Jean Palussière
    • 2
  1. 1.Institut de Mathématiques de Bordeaux UMR 5251Université de Bordeaux and INRIA Bordeaux Sud-OuestTalenceFrance
  2. 2.Institut BergoniéBordeauxFrance

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