High-grade brain gliomas, the most common of which is glioblastoma multiforme (GBM), have terrible prognosis and a median patient survival of about 12 months. Although combinations of surgical removal, radiotherapy and chemotherapy are used in the clinical practice, a fundamental and persistent limitation in treating these aggressive tumors is that they tend to infiltrate into normal tissue well beyond margins visible via imaging. Since assessing the spatial extent of tumor infiltration is nearly impossible using current radiologic reading practices, clinical treatment tends to be restricted to parts that are deemed to be clearly malignant, frequently only the enhancing tumor. This failure to aggressively treat the infiltrating tumor accelerates tumor recurrence, and eventually patient death. This proposal aims to develop computational modeling and image analysis methods that will improve our ability to estimate GBM infiltration, as well as to predict tissue that is likelyto present fastest tumor recurrence, thereby eventually opening the way for more aggressive, yet targeted, treatment, such as targeted aggressive surgical removal and/or radiosurgery. To achieve our goal, we will integrate information from several sources: 1) advanced multi-parametric imaging, which captures many aspects of tumor anatomy and physiology~ 2) computational modeling of tumor growth and infiltration~ 3) machine learning methods which, after appropriate training, can learn subtle and potentially complex imaging phenotypes of infiltrating tumors~ 4) statistical atlases, which capture population-based trends that can offer additional insights into tumor growth, such as relationship of infiltration to vasculature and to white matter fiber pathways~ 5) data from one of the largest patient populations having advanced imaging, genotyping, follow-up till tumor recurrence, and histological analysis.

Public Health Relevance

This project will develop advance computational imaging and informatics methods for analysis of high-grade gliomas (brain tumors). It will compile a unique database of data from several hundred patients and will construct predictive models of infiltrating malignant tumor and of later recurrence. Therefore, it will pave the way for more refined and targeted treatments of peritumoral brain tissue, which is where most tumor recurrence occurs.

National Institute of Health (NIH)
Research Project (R01)
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Biomedical Imaging Technology Study Section (BMIT)
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Fountain, Jane W
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University of Pennsylvania
Schools of Medicine
United States
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Akbari, Hamed; Macyszyn, Luke; Da, Xiao et al. (2014) Pattern analysis of dynamic susceptibility contrast-enhanced MR imaging demonstrates peritumoral tissue heterogeneity. Radiology 273:502-10
Kwon, Dongjin; Shinohara, Russell T; Akbari, Hamed et al. (2014) Combining generative models for multifocal glioma segmentation and registration. Med Image Comput Comput Assist Interv 17:763-70
Kwon, Dongjin; Niethammer, Marc; Akbari, Hamed et al. (2014) PORTR: Pre-operative and post-recurrence brain tumor registration. IEEE Trans Med Imaging 33:651-67
Gooya, Ali; Biros, George; Davatzikos, Christos (2011) Deformable registration of glioma images using EM algorithm and diffusion reaction modeling. IEEE Trans Med Imaging 30:375-90
Zacharaki, Evangelia I; Kanas, Vasileios G; Davatzikos, Christos (2011) Investigating machine learning techniques for MRI-based classification of brain neoplasms. Int J Comput Assist Radiol Surg 6:821-8
Gooya, Ali; Pohl, Kilian M; Bilello, Michel et al. (2011) Joint segmentation and deformable registration of brain scans guided by a tumor growth model. Med Image Comput Comput Assist Interv 14:532-40
Zacharaki, Evangelia I; Hogea, Cosmina S; Shen, Dinggang et al. (2009) Non-diffeomorphic registration of brain tumor images by simulating tissue loss and tumor growth. Neuroimage 46:762-74
Zacharaki, Evangelia I; Wang, Sumei; Chawla, Sanjeev et al. (2009) Classification of brain tumor type and grade using MRI texture and shape in a machine learning scheme. Magn Reson Med 62:1609-18
Hogea, Cosmina; Davatzikos, Christos; Biros, George (2008) An image-driven parameter estimation problem for a reaction-diffusion glioma growth model with mass effects. J Math Biol 56:793-825
Zacharaki, Evangelia I; Shen, Dinggang; Lee, Seung-Koo et al. (2008) ORBIT: a multiresolution framework for deformable registration of brain tumor images. IEEE Trans Med Imaging 27:1003-17

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