Predicting the Location of Glioma Recurrence After a Resection Surgery

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 different time points, the estimation of the tumor infiltration map, and the prediction of tumor regrowth using a reaction-diffusion 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 radiologically non-visible infiltrative extent of the tumor. Such information could pave the way for model-based personalization of treatment planning in a near future.
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Communication dans un congrès
Proceedings of 2nd International MICCAI Workshop on Spatiotemporal Image Analysis for Longitudinal and Time-Series Image Data (STIA'12), 2012, Nice, France. Springer, 0000, LNCS. 〈10.1007/978-3-642-33555-6_10〉
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Erin Stretton, Emmanuel Mandonnet, Ezequiel Geremia, Bjoern H. Menze, Hervé Delingette, et al.. Predicting the Location of Glioma Recurrence After a Resection Surgery. Proceedings of 2nd International MICCAI Workshop on Spatiotemporal Image Analysis for Longitudinal and Time-Series Image Data (STIA'12), 2012, Nice, France. Springer, 0000, LNCS. 〈10.1007/978-3-642-33555-6_10〉. 〈hal-00813870〉

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