Personalized Radiotherapy Planning Based on a Computational Tumor Growth Model

Matthieu Le, Herve Delingette, Jayashree Kalpathy-Cramer, Elizabeth R. Gerstner, Tracy Batchelor, Jan Unkelbach, Nicholas Ayache

Research output: Contribution to journalArticlepeer-review

31 Scopus citations


In this article, we propose a proof of concept for the automatic planning of personalized radiotherapy for brain tumors. A computational model of glioblastoma growth is combined with an exponential cell survival model to describe the effect of radiotherapy. The model is personalized to the magnetic resonance images (MRIs) of a given patient. It takes into account the uncertainty in the model parameters, together with the uncertainty in the MRI segmentations. The computed probability distribution over tumor cell densities, together with the cell survival model, is used to define the prescription dose distribution, which is the basis for subsequent Intensity Modulated Radiation Therapy (IMRT) planning. Depending on the clinical data available, we compare three different scenarios to personalize the model. First, we consider a single MRI acquisition before therapy, as it would usually be the case in clinical routine. Second, we use two MRI acquisitions at two distinct time points in order to personalize the model and plan radiotherapy. Third, we include the uncertainty in the segmentation process. We present the application of our approach on two patients diagnosed with high grade glioma. We introduce two methods to derive the radiotherapy prescription dose distribution, which are based on minimizing integral tumor cell survival using the maximum a posteriori or the expected tumor cell density. We show how our method allows the user to compute a patient specific radiotherapy planning conformal to the tumor infiltration. We further present extensions of the method in order to spare adjacent organs at risk by re-distributing the dose. The presented approach and its proof of concept may help in the future to better target the tumor and spare organs at risk.

Original languageEnglish (US)
Article number7738594
Pages (from-to)815-825
Number of pages11
JournalIEEE Transactions on Medical Imaging
Issue number3
StatePublished - Mar 2017


  • Glioblastoma
  • Radiotherapy planning
  • computational tumor growth model
  • personalization
  • segmentation
  • uncertainty

ASJC Scopus subject areas

  • Software
  • Radiological and Ultrasound Technology
  • Computer Science Applications
  • Electrical and Electronic Engineering


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