Modeling Glioma Growth and Mass Effect in 3D MR Images of the Brain 1st Edition by Cosmina Hogea, Christos Davatzikos, George Biros ISBN 9783540757573

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ISBN 13: 9783540757573
Author: Cosmina Hogea, Christos Davatzikos, George Biros

In this article, we propose a framework for modeling glioma growth and the subsequent mechanical impact on the surrounding brain tissue (mass-effect) in a medical imaging context. Glioma growth is modeled via nonlinear reaction-advection-diffusion, with a two-way coupling with the underlying tissue elastic deformation. Tumor bulk and infiltration and subsequent mass-effects are not regarded separately, but captured by the model itself in the course of its evolution. Our formulation is fully Eulerian and naturally allows for updating the tumor diffusion coefficient following structural displacements caused by tumor growth/infiltration. We show that model parameters can be estimated via optimization based on imaging data, using efficient solution algorithms on regular grids. We test the model and the automatic optimization framework on real brain tumor data sets, achieving significant improvement in landmark prediction compared to a simplified purely mechanical approach.

 Modeling Glioma Growth and Mass Effect in 3D MR Images of the Brain 1st Table of contents:

• Introduction
  1.1 Motivation and Background
  1.2 Importance of Modeling Glioma Growth and Mass Effect
  1.3 Challenges in Glioma Growth Modeling with MRI
  1.4 Overview of Mass Effect in Glioma Progression
  1.5 Key Contributions and Objectives of the Paper
  1.6 Structure of the Paper

• Preliminaries
  2.1 Glioma: Types, Characteristics, and Clinical Relevance
  2.2 Imaging Techniques: 3D MRI in Brain Tumor Analysis
  2.3 Tumor Growth Models in Medical Imaging
  2.4 Mass Effect: Definition, Impact, and Measurement
  2.5 Related Work on Glioma Growth Modeling and Mass Effect Studies

• Modeling Glioma Growth
  3.1 Mathematical and Computational Models of Tumor Growth
  3.2 Mechanisms of Glioma Growth: Proliferation, Necrosis, and Angiogenesis
  3.3 Modeling Tumor Dynamics in 3D Space
  3.4 Application of Diffusion Models and Reaction-Diffusion Equations
  3.5 Incorporating Spatial and Temporal Data for Accurate Modeling

• Mass Effect and Its Impact on Surrounding Brain Structures
  4.1 Defining Mass Effect in the Context of Gliomas
  4.2 Physiological and Structural Changes Due to Mass Effect
  4.3 Mathematical Representation of Mass Effect in 3D Brain Imaging
  4.4 Modeling the Compression of Healthy Tissue by Tumor Growth
  4.5 Impact on Brain Function and Clinical Implications

• Methodology
  5.1 MRI Data Acquisition and Preprocessing for Glioma Analysis
  5.2 Tumor Segmentation and Boundary Detection from MRI Scans
  5.3 Incorporating Mass Effect into Tumor Growth Models
  5.4 Coupling Tumor Growth and Mass Effect in 3D Space
  5.5 Computational Framework and Algorithm Development

• Experimental Results and Evaluation
  6.1 Dataset Description and MRI Scans Used for Glioma Growth Modeling
  6.2 Quantitative Evaluation Metrics (e.g., Volume, Growth Rate, Mass Effect Evaluation)
  6.3 Comparison of Model Predictions with Clinical Data
  6.4 Case Studies: Glioma Growth and Mass Effect in Different Patient Scenarios
  6.5 Sensitivity to MRI Data Quality and Model Parameters

• Applications in Clinical Practice
  7.1 Predicting Glioma Progression for Treatment Planning
  7.2 Role in Surgical Planning and Resection Strategy
  7.3 Assisting in Radiation Therapy and Chemotherapy Decision-Making
  7.4 Longitudinal Studies for Monitoring Tumor Response to Treatment

• Discussion
  8.1 Insights from Glioma Growth and Mass Effect Modeling
  8.2 Strengths and Limitations of the Proposed Model
  8.3 Challenges in Generalizing the Model Across Different Tumor Types
  8.4 Future Directions in Tumor Growth and Mass Effect Modeling
  8.5 Potential for Integration with AI and Machine Learning Models

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