Radiotherapy planning for glioblastoma based on a tumor growth model: improving target volume delineation
Jan Unkelbach,Bjoern H. Menze,Bjoern H. Menze,Ender Konukoglu,Florian Dittmann,Matthieu Le,Matthieu Le,Nicholas Ayache,Helen A. Shih +8 more
TLDR
The tumor growth model provides a method to account for anisotropic growth patterns of glioma, and may therefore provide a tool to make target delineation more objective and automated.Abstract:
Glioblastoma di ffer from many other tumors in the sense that they grow in filtratively into the brain tissue instead of forming a solid tumor mass with a de fined boundary. Only the part of the tumor with high tumor cell density can be localized through imaging directly. In contrast, brain tissue in filtrated by tumor cells at low density appears normal on current imaging modalities. In current clinical practice, a uniform margin, typically two centimeters, is applied to account for microscopic spread of disease that is not directly assessable through imaging. The current treatment planning procedure can potentially be improved by accounting for the anisotropy of tumor growth, which arises from di erent factors: Anatomical barriers such as the falx cerebri represent boundaries for migrating tumor cells. In addition, tumor cells primarily spread in white matter and in ltrate gray matter at lower rate. We investigate the use of a phenomenological tumor growth model for treatment planning. The model is based on the Fisher-Kolmogorov equation, which formalizes these growth characteristics and estimates the spatial distribution of tumor cells in normal appearing regions of the brain. The target volume for radiotherapy planning can be de fined as an isoline of the simulated tumor cell density. This paper analyzes the model with respect to implications for target volume de finition and identi fies its most critical components. A retrospective study involving 10 glioblastoma patients treated at our institution has been performed. To illustrate the main findings of the study, a detailed case study is presented for a glioblastoma located close to the falx. In this situation, the falx represents a boundary for migrating tumor cells, whereas the corpus callosum provides a route for the tumor to spread to the contralateral hemisphere. We further discuss the sensitivity of the model with respect to the input parameters. Correct segmentation of the brain appears to be the most crucial model input. We conclude that the tumor growth model provides a method to account for anisotropic growth patterns of glioma, and may therefore provide a tool to make target delineation more objective and automated.read more
Citations
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Journal ArticleDOI
Personalized Radiotherapy Design for Glioblastoma: Integrating Mathematical Tumor Models, Multimodal Scans, and Bayesian Inference
Jana Lipkova,Panagiotis Angelikopoulos,Stephen Wu,Esther Alberts,Benedikt Wiestler,Christian Diehl,Christine Preibisch,Thomas Pyka,Stephanie E. Combs,Panagiotis E. Hadjidoukas,Koen Van Leemput,Petros Koumoutsakos,John Lowengrub,Bjoern H. Menze +13 more
TL;DR: In this article, a Bayesian machine learning framework for the rational design of improved, personalized radiotherapy plans using mathematical modeling and patient multimodal medical scans was proposed, which is applicable to standard clinical settings.
Journal ArticleDOI
Contextual Atlas Regression Forests: Multiple-Atlas-Based Automated Dose Prediction in Radiation Therapy
Chris McIntosh,Thomas G. Purdie +1 more
TL;DR: A proof-of-concept for a method to automatically infer the radiation dose directly from the patient's treatment planning image based on a database of previous patients with corresponding clinical treatment plans and an automatic atlas-selection metric that is tailored to dose prediction.
A generative approach for image-based modeling of tumor growth
Bjoern H. Menze,Koen Van Leemput,Antti Honkela,Ender Konukoglu,Marc-André Weber,Nicholas Ayache,Polina Golland +6 more
TL;DR: A joint generative model of tumor growth and of image observation that naturally handles multimodal and longitudinal data is proposed that can be used for integrating information from different multi-modal imaging protocols and can be adapted to other tumor growth models.
Journal ArticleDOI
Personalized Radiotherapy Planning Based on a Computational Tumor Growth Model
Matthieu Le,Hervé Delingette,Jayashree Kalpathy-Cramer,Elizabeth R. Gerstner,Tracy T. Batchelor,Jan Unkelbach,Nicholas Ayache +6 more
TL;DR: Two methods to derive the radiotherapy prescription dose distribution are introduced, which are based on minimizing integral tumor cell survival using the maximum a posteriori or the expected tumor cell density and it is shown how the method allows the user to compute a patient specific radiotherapy planning conformal to the tumor infiltration.
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