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Image based modeling of tumor growth

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Abstract

Tumors are a main cause of morbidity and mortality worldwide. Despite the efforts of the clinical and research communities, little has been achieved in the past decades in terms of improving the treatment of aggressive tumors. Understanding the underlying mechanism of tumor growth and evaluating the effects of different therapies are valuable steps in predicting the survival time and improving the patients’ quality of life. Several studies have been devoted to tumor growth modeling at different levels to improve the clinical outcome by predicting the results of specific treatments. Recent studies have proposed patient-specific models using clinical data usually obtained from clinical images and evaluating the effects of various therapies. The aim of this review is to highlight the imaging role in tumor growth modeling and provide a worthwhile reference for biomedical and mathematical researchers with respect to tumor modeling using the clinical data to develop personalized models of tumor growth and evaluating the effect of different therapies.

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Authors and Affiliations

  1. Division of Biomechanics, Department of Mechanical Engineering, Sahand University of Technology, East Azerbaijan, Tabriz, Iran

    N. Meghdadi, H. Niroomand-Oscuii & F. Ghalichi

  2. Division of Nuclear Medicine, Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, 21287-0807, USA

    M. Soltani

  3. Department of Mechanical Engineering, K. N. T. University of Technology, Tehran, Iran

    M. Soltani

  4. Cancer Biology Research Center, Tehran University of Medical Sciences, Tehran, Iran

    M. Soltani

  5. Computational Medicine Institute, Tehran, Iran

    N. Meghdadi & M. Soltani

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  1. N. Meghdadi
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  2. M. Soltani
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Meghdadi, N., Soltani, M., Niroomand-Oscuii, H. et al. Image based modeling of tumor growth. Australas Phys Eng Sci Med 39, 601–613 (2016). https://doi.org/10.1007/s13246-016-0475-5

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