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Proceedings Paper

Subject-specific brain tumor growth modelling via an efficient Bayesian inference framework
Author(s): Yongjin Chang; Gregory C. Sharp; Quanzheng Li; Helen A. Shih; Georges El Fakhri; Jong Beom Ra; Jonghye Woo
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Paper Abstract

An accurate prediction of brain tumor progression is crucial for optimized treatment of the tumors. Gliomas are primarily treated by combining surgery, external beam radiotherapy, and chemotherapy. Among them, radiotherapy is a non-invasive and effective therapy, and an understanding of tumor growth will allow better therapy planning. In particular, estimating parameters associated with tumor growth, such as the diffusion coefficient and proliferation rate, is crucial to accurately characterize physiology of tumor growth and to develop predictive models of tumor infiltration and recurrence. Accurate parameter estimation, however, is a challenging task due to inaccurate tumor boundaries and the approximation of the tumor growth model. Here, we introduce a Bayesian framework for a subject-specific tumor growth model that estimates the tumor parameters effectively. This is achieved by using an improved elliptical slice sampling method based on an adaptive sample region. Experimental results on clinical data demonstrate that the proposed method provides a higher acceptance rate, while preserving the parameter estimation accuracy, compared with other state-of-the-art methods such as Metropolis-Hastings and elliptical slice sampling without any modification. Our approach has the potential to provide a method to individualize therapy, thereby offering an optimized treatment.

Paper Details

Date Published: 2 March 2018
PDF: 6 pages
Proc. SPIE 10574, Medical Imaging 2018: Image Processing, 105742I (2 March 2018); doi: 10.1117/12.2293145
Show Author Affiliations
Yongjin Chang, KAIST (Korea, Republic of)
Gregory C. Sharp, Massachusetts General Hospital, Harvard Medical School (United States)
Quanzheng Li, Massachusetts General Hospital, Harvard Medical School (United States)
Helen A. Shih, Massachusetts General Hospital, Harvard Medical School (United States)
Georges El Fakhri, Massachusetts General Hospital, Harvard Medical School (United States)
Jong Beom Ra, Massachusetts General Hospital, Harvard Medical School (United States)
Jonghye Woo, Massachusetts General Hospital, Harvard Medical School (United States)


Published in SPIE Proceedings Vol. 10574:
Medical Imaging 2018: Image Processing
Elsa D. Angelini; Bennett A. Landman, Editor(s)

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