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Predicting the Location of Glioma Recurrence After a Resection Surgery

By Erin Stretton, Emmanuel Mandonnet, Ezequiel Geremia, Bjoern H. Menze, Hervé Delingette and Nicholas Ayache

Abstract

International audienceWe 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

Topics: [ INFO.INFO-IM ] Computer Science [cs]/Medical Imaging, [ INFO.INFO-TI ] Computer Science [cs]/Image Processing, [ INFO.INFO-TS ] Computer Science [cs]/Signal and Image Processing, [ SPI.SIGNAL ] Engineering Sciences [physics]/Signal and Image processing, [ INFO.INFO-MO ] Computer Science [cs]/Modeling and Simulation
Publisher: Springer
Year: 2012
DOI identifier: 10.1007/978-3-642-33555-6_10
OAI identifier: oai:HAL:hal-00813870v1
Provided by: Hal-Diderot

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