TY - GEN
T1 - Predicting the location of glioma recurrence after a resection surgery
AU - Stretton, Erin
AU - Mandonnet, Emmanuel
AU - Geremia, Ezequiel
AU - Menze, Bjoern H.
AU - Delingette, Hervé
AU - Ayache, Nicholas
PY - 2012
Y1 - 2012
N2 - 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.
AB - 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.
UR - https://www.scopus.com/pages/publications/84867890359
U2 - 10.1007/978-3-642-33555-6_10
DO - 10.1007/978-3-642-33555-6_10
M3 - Conference contribution
AN - SCOPUS:84867890359
SN - 9783642335549
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 113
EP - 123
BT - Spatio-temporal Image Analysis for Longitudinal and Time-Series Image Data - Second International Workshop, STIA 2012, Held in Conjunction with MICCAI 2012, Proceedings
T2 - 2nd International Workshop on Spatiotemporal Image Analysis for Longitudinal and Time-Series Image Data, STIA 2012, Held in Conjunction with the International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2012
Y2 - 1 October 2012 through 1 October 2012
ER -