Surgical and radiosurgical planning in tumor patients is a very difficult task, requiring the surgeon to mentally form a picture of the complex three-dimensional geometry of anatomical and functional units of the brain, not only around the tumor, but also along all possible paths that access the tumor. Moreover, the surgeon must evaluate each possible surgical strategy in terms of potential complications and probability of a successful outcome. Statistical atlases of the brain can be very helpful in this procedure, as they provide a plethora of useful information, ranging from functional neuroanatomy to statistical models linking tumor parameters, surgical approach, and outcomes. Adapting a statistical atlas of neuroanatomy to a patient's images is a very challenging task, due to the complexity of human brain anatomy and to the confounding effects of edema and extreme deformations in the vicinity of the tumor. This task is undertaken by the proposed project. We propose to build and extensively validate computational models and algorithms that 1) reconstruct a mathematical representation of a tumor patient's anatomy from tomographic images, 2) estimate the deformation that has been induced by tumor growth, 3) adapt anatomical atlases to the patient's images, accounting for tumor growth and concomitant healthy tissue deformation. We also propose to demonstrate the utility of this methodology in building statistical atlases that link position and shape of the tumor, surgical approach, and surgical outcome, by applying it to a small group of patients with tumors near motor and speech areas and correlating surgical outcome with tumor position and geometry. The proposed methodology combines a biomechanical model of the brain with a statistical model that captures systematic relationships between a patient's anatomy prior to tumor growth and the patient's anatomy after tumor growth, thereby mathematically parameterizing the underlying brain deformation. The ability to utilize mathematical models of brain anatomy and to select from a group of standard approaches according to an objective prediction of the amount of brain injury each entails, will significantly improve clinical outcome of common neurosurgical procedures.

Agency
National Institute of Health (NIH)
Institute
National Institute of Neurological Disorders and Stroke (NINDS)
Type
Research Project (R01)
Project #
5R01NS042645-04
Application #
6744427
Study Section
Diagnostic Imaging Study Section (DMG)
Program Officer
Tagle, Danilo A
Project Start
2002-06-01
Project End
2006-04-30
Budget Start
2004-05-01
Budget End
2005-04-30
Support Year
4
Fiscal Year
2004
Total Cost
$251,295
Indirect Cost
Name
University of Pennsylvania
Department
Radiation-Diagnostic/Oncology
Type
Schools of Medicine
DUNS #
042250712
City
Philadelphia
State
PA
Country
United States
Zip Code
19104
Rathore, Saima; Bakas, Spyridon; Pati, Sarthak et al. (2018) Brain Cancer Imaging Phenomics Toolkit (brain-CaPTk): An Interactive Platform for Quantitative Analysis of Glioblastoma. Brainlesion (2017) 10670:133-145
Fathi Kazerooni, Anahita; Nabil, Mahnaz; Zeinali Zadeh, Mehdi et al. (2018) Characterization of active and infiltrative tumorous subregions from normal tissue in brain gliomas using multiparametric MRI. J Magn Reson Imaging 48:938-950
Davatzikos, Christos; Rathore, Saima; Bakas, Spyridon et al. (2018) Cancer imaging phenomics toolkit: quantitative imaging analytics for precision diagnostics and predictive modeling of clinical outcome. J Med Imaging (Bellingham) 5:011018
Rathore, Saima; Akbari, Hamed; Rozycki, Martin et al. (2018) Radiomic MRI signature reveals three distinct subtypes of glioblastoma with different clinical and molecular characteristics, offering prognostic value beyond IDH1. Sci Rep 8:5087
Akbari, Hamed; Bakas, Spyridon; Pisapia, Jared M et al. (2018) In vivo evaluation of EGFRvIII mutation in primary glioblastoma patients via complex multiparametric MRI signature. Neuro Oncol 20:1068-1079
Bakas, Spyridon; Akbari, Hamed; Pisapia, Jared et al. (2017) In Vivo Detection of EGFRvIII in Glioblastoma via Perfusion Magnetic Resonance Imaging Signature Consistent with Deep Peritumoral Infiltration: The ?-Index. Clin Cancer Res 23:4724-4734
Mang, Andreas; Ruthotto, Lars (2017) A LAGRANGIAN GAUSS-NEWTON-KRYLOV SOLVER FOR MASS- AND INTENSITY-PRESERVING DIFFEOMORPHIC IMAGE REGISTRATION. SIAM J Sci Comput 39:B860-B885
Mang, Andreas; Biros, George (2017) A SEMI-LAGRANGIAN TWO-LEVEL PRECONDITIONED NEWTON-KRYLOV SOLVER FOR CONSTRAINED DIFFEOMORPHIC IMAGE REGISTRATION. SIAM J Sci Comput 39:B1064-B1101
Sotiras, Aristeidis; Toledo, Jon B; Gur, Raquel E et al. (2017) Patterns of coordinated cortical remodeling during adolescence and their associations with functional specialization and evolutionary expansion. Proc Natl Acad Sci U S A 114:3527-3532
Mang, Andreas; Biros, George (2016) Constrained H1-regularization schemes for diffeomorphic image registration. SIAM J Imaging Sci 9:1154-1194

Showing the most recent 10 out of 39 publications