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A Novel High-resolution In vivo Imaging Technique to Study the Dynamic Response of Intracranial Structures to Tumor Growth and Therapeutics
Published on: June 16, 2013
In vivo mathematical modeling of tumor growth from imaging data: soon to come in the future?
F Cornelis1, O Saut, P Cumsille
1Bordeaux University, IMB, UMR 5251, 33400 Talence, France. francois.cornelis@chu-bordeaux.fr
Abstract:
The future challenges in oncology imaging are to assess the response to treatment even earlier. As an addition to functional imaging, mathematical modeling based on the imaging is an alternative, cross-disciplinary area of development. Modeling was developed in oncology not only in order to understand and predict tumor growth, but also to anticipate the effects of targeted and untargeted therapies. A very wide range of these models exist, involving many stages in the progression of tumors. Few models, however, have been proposed to reproduce in vivo tumor growth because of the complexity of the mechanisms involved. Morphological imaging combined with "spatial" models appears to perform well although functioning imaging could still provide further information on metabolism and the micro-architecture. The combination of imaging and modeling can resolve complex problems and describe many facets of tumor growth or response to treatment. It is now possible to consider its clinical use in the medium term. This review describes the basic principles of mathematical modeling and describes the advantages, limitations and future prospects for this in vivo approach based on imaging data.
Insights
Mathematical modeling combined with oncology imaging offers a promising approach for early assessment of treatment response. This interdisciplinary field aids in understanding tumor growth and predicting therapy effects for potential clinical use.
Area of Science:
- Oncology imaging and mathematical modeling.
- Interdisciplinary approach to cancer research.
Background:
- Early assessment of treatment response is a key challenge in oncology.
- Mathematical modeling aids in understanding tumor growth and therapy effects.
Purpose of the Study:
- To review the principles, advantages, limitations, and future prospects of in vivo imaging combined with mathematical modeling in oncology.
- To explore the potential clinical application of this integrated approach.
Main Methods:
- Review of existing mathematical models for tumor growth and treatment response.
- Integration of morphological and functional imaging data with spatial and metabolic models.
- Analysis of in vivo tumor growth mechanisms.
Main Results:
- Combination of imaging and modeling can resolve complex problems in tumor growth and treatment response.
- Morphological imaging with spatial models shows promise, while functional imaging offers metabolic insights.
- The integrated approach is considered for medium-term clinical use.
Conclusions:
- Mathematical modeling integrated with oncology imaging presents a powerful tool for advancing cancer research and clinical practice.
- Further development is needed to fully realize the potential of this cross-disciplinary field for in vivo applications.
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