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UID:8208@i2m.univ-amu.fr
DTSTART;TZID=Europe/Paris:20141215T140000
DTEND;TZID=Europe/Paris:20141215T160000
DTSTAMP:20241208T202046Z
URL:https://www.i2m.univ-amu.fr/evenements/modelling-of-metastatic-growth-
 and-in-vivo-imaging-niklas-hartung/
SUMMARY:Niklas Hartung (I2M\, Aix-Marseille Université): Modelling of Meta
 static Growth and In Vivo Imaging
DESCRIPTION:Niklas Hartung: http://www.theses.fr/2014AIXM4763\n\nModelling 
 of Metastatic Growth and In Vivo Imaging\nMetastasis is one of the major p
 roblems of cancer because metastases areoften difficult to detect by clini
 cal imaging and may develop rapidly. With the help of mathematical modelli
 ng\, we hope to developnew tools capable of anticipating the metastatic st
 ate of a patient.The first two parts of this thesis are dedicated to devel
 oping such a tool\, destined for a preclinical oreven clinical use. As tum
 our growth dynamics vary strongly between individuals and since observatio
 ns are often sparse andnoisy\, we need to consider computationally expensi
 ve statistical tools.In the first part\, we extend an approach introduced 
 by Iwata et al. and developed by Barbolosi et al. In particular\, wepropos
 e a more efficient numerical resolution based on a model reformulation int
 o a Volterra integral equation of convolutiontype. This reformulation also
  permits to prove theoretical model properties (regularity and identifiabi
 lity). Moreover\, we study a stochastic generalisation of this determinist
 ic model.In the second part\, we will show that our approach is suitable f
 or the description of experimental data on tumour-bearing mice.Using the s
 tatistical framework of nonlinear mixed-effects modelling\, we build a met
 astatic model that is identifiable fromour data. We then interpret the res
 ults biologically.The last part of this thesis contains several results ob
 tained in collaboration with biologists. We have started to model tumourgr
 owth with data obtained from SPECT imaging\, using a model by Gyllenberg a
 nd Webb. Also\, in order to improve the precision ofSPECT data\, we have t
 ested contour detection methods via finite volume methods based on DDFV sc
 hemes.\n\nSous la direction de Florence Hubert et de Guillemette Chapuisat
 .\n\nà Aix-Marseille \, dans le cadre de  Ecole Doctorale Mathématiques 
 et Informatique de Marseille (Marseille) .\n\nLe président du jury était
  Jean Clairambault.\n\nLe jury était composé de Benjamin Ribba\, Assia B
 enabdallah.\n\nLes rapporteurs étaient Anna Marciniak-Czochra\, Marc Lavi
 elle.\n\n*Membres du jury :\nRapporteurs :\n- Marc LAVIELLE\, Université 
 Paris-Sud\n- Anna MARCINIAK-CZOCHRA\, Universität Heidelberg\n\nExaminate
 urs :\n- Assia BENABDALLAH\, Aix-Marseille Université\n- Jean CLAIRAMBAUL
 T\, INRIA Paris-Rocquencourt\n- Dietmar KRÖNER\, Universität Freiburg\n-
  Benjamin RIBBA\, INRIA Sophia-Antipolis\n\nDirectrices de thèse :\n- Gui
 llemette CHAPUISAT\, Aix-Marseille Université\n- Florence HUBERT\, Aix-Ma
 rseille Université
ATTACH;FMTTYPE=image/jpeg:https://www.i2m.univ-amu.fr/wp-content/uploads/2
 020/01/Niklas_Hartung.jpg
CATEGORIES:Soutenance de thèse,Analyse Appliquée
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DTSTART:20141026T020000
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