Towards an Identification of Tumor Growth Parameters from Time Series of Images 1st Edition by Ender Konukoglu, Olivier Clatz, Pierre Yves Bondiau, Maxime Sermesant, Herve Delingette, Nicholas Ayache ISBN 9783540757573

Towards an Identification of Tumor Growth Parameters from Time Series of Images 1st Edition by Ender Konukoglu, Olivier Clatz, Pierre Yves Bondiau, Maxime Sermesant, Herve Delingette, Nicholas Ayache – Ebook PDF Instant Download/Delivery. 9783540757573
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ISBN 10: 
ISBN 13: 9783540757573
Author: Ender Konukoglu, Olivier Clatz, Pierre Yves Bondiau, Maxime Sermesant, Herve Delingette, Nicholas Ayache

In cancer treatment, understanding the aggressiveness of the tumor is essential in therapy planning and patient follow-up. In this article, we present a novel method for quantifying the speed of invasion of gliomas in white and grey matter from time series of magnetic resonance (MR) images. The proposed approach is based on mathematical tumor growth models using the reaction-diffusion formalism. The quantification process is formulated by an inverse problem and solved using anisotropic fast marching method yielding an efficient algorithm. It is tested on a few images to get a first proof of concept with promising new results.

Towards an Identification of Tumor Growth Parameters from Time Series of Images 1st Table of contents:

• Introduction

  • 1.1 Background and Motivation
  • 1.2 Importance of Tumor Growth Modeling
  • 1.3 Overview of Time Series Imaging Data
  • 1.4 Objectives and Scope of the Study

• Literature Review

  • 2.1 Tumor Growth Models: A Historical Perspective
  • 2.2 Time Series Analysis in Medical Imaging
  • 2.3 Existing Methods for Tumor Growth Estimation
  • 2.4 Challenges in Identifying Tumor Growth Parameters

• Methodology

  • 3.1 Overview of Tumor Growth Modeling
  • 3.2 Time Series Imaging Data Collection and Preprocessing
  • 3.3 Parametric Models for Tumor Growth
    • 3.3.1 Logistic Growth Model
    • 3.3.2 Exponential Growth Model
    • 3.3.3 Gompertz Growth Model
  • 3.4 Parameter Estimation Techniques
    • 3.4.1 Curve Fitting
    • 3.4.2 Bayesian Inference
  • 3.5 Incorporating Uncertainty and Variability in Growth Parameters

• Data Acquisition and Preprocessing

  • 4.1 Data Collection: Imaging Modalities and Protocols
  • 4.2 Temporal Resolution and Data Synchronization
  • 4.3 Image Preprocessing for Tumor Segmentation
  • 4.4 Time Series Alignment and Interpolation

• Parameter Identification Process

  • 5.1 Definition and Extraction of Tumor Features
  • 5.2 Model Selection and Fitting Strategies
  • 5.3 Sensitivity Analysis of Tumor Growth Parameters
  • 5.4 Temporal Dynamics of Tumor Growth
  • 5.5 Validation of Parameter Estimation

• Results

  • 6.1 Performance of Different Growth Models
  • 6.2 Estimated Tumor Growth Parameters for Sample Cases
  • 6.3 Comparison with Clinical Observations
  • 6.4 Model Accuracy and Limitations

• Discussion

  • 7.1 Implications of Tumor Growth Parameter Identification
  • 7.2 Clinical Relevance of Identified Parameters
  • 7.3 Influence of Imaging Quality on Results
  • 7.4 Future Directions in Tumor Growth Modeling

• Conclusion

  • 8.1 Summary of Findings
  • 8.2 Impact on Cancer Treatment Planning
  • 8.3 Closing Remarks

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