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Predicting the Location of Glioma Recurrence After a Resection Surgery

E. Stretton and E. Mandonnet and E. Geremia and B. H. Menze and H. Delingette and N. Ayache
Proc MICCAI-STIA (Workshop on Spatiotemporal Image Analysis for Longitudinal and Time-Series Image Data)
Nice, France 2012

Abstract

We propose a method for estimating the location of glioma recurrence after surgical resection. This method consists of a pipeline including the registration of images at di erent time points, the estima- tion of the tumor in ltration map, and the prediction of tumor regrowth using a reaction-di usion model. A data set acquired on a patient with a low-grade glioma and post surgery MRIs is considered to evaluate the accuracy of the estimated recurrence locations found using our method. We observed good agreement in tumor volume prediction and qualitative matching in regrowth locations. Therefore, the proposed method seems adequate for modeling low-grade glioma recurrence. This tool could help clinicians anticipate tumor regrowth and better characterize the radiolog- ically non-visible in ltrative extent of the tumor. Such information could pave the way for model-based personalization of treatment planning in a near future.


Link to publisher's page
@InProceedings{eth_biwi_00988,
  author = {E. Stretton and E. Mandonnet and E. Geremia and B. H. Menze and H. Delingette and N. Ayache},
  title = {Predicting the Location of Glioma Recurrence After a Resection Surgery},
  booktitle = {Proc MICCAI-STIA (Workshop on Spatiotemporal Image Analysis for Longitudinal and Time-Series Image Data)},
  year = {2012},
  pages = {12},
  series = {LNCS},
  keywords = {}
}