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Brain tumor growth simulation: model validation through uncertainty quantification

Author

Listed:
  • N. Meghdadi

    (Sahand University of Technology)

  • H. Niroomand-Oscuii

    (Sahand University of Technology)

  • M. Soltani

    (K. N. Toosi University of Technology
    Johns Hopkins University, School of Medicine)

  • F. Ghalichi

    (Sahand University of Technology)

  • M. Pourgolmohammad

    (Sahand University of Technology)

Abstract

Brain tumors are one of the main worldwide causes of mortality and morbidity and a critical issue in health risk. Tumor growth prediction is a proper method for better understanding the phenomena and choosing the appropriate therapy for patients. Since tumors’ physiological and morphological properties vary significantly in different individuals, using patient specific data is valuable for modelling tumor growth in staging and personalized-therapy planning. However, the validity of the models should be evaluated for their precision assessment based on the decision criteria. There are different sources of uncertainties affecting model prediction accuracy and decision making for the therapy. In this paper, an image-based tumor growth model is evaluated by taking into account uncertainties in the model parameters. The proposed reaction–diffusion model integrates cancerous cell proliferation and invasion through reaction and diffusion terms, respectively. Uncertainties in diffusion and proliferation coefficients were analyzed through Monte Carlo simulation. The time needed for tumor to grow to its fatal size was estimated through numerical solution of the model. Comparison of the predicted time distribution with and without considering uncertainties in model parameters shows a decrease in dispersity of predicted data that highlights the importance of uncertainty. Also, the wide range for survival time shows the importance of choosing proper parameters in order to enhance model accuracy. The recommendations were made for increasing the validity of the tumor growth models.

Suggested Citation

  • N. Meghdadi & H. Niroomand-Oscuii & M. Soltani & F. Ghalichi & M. Pourgolmohammad, 2017. "Brain tumor growth simulation: model validation through uncertainty quantification," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 8(3), pages 655-662, September.
    • Handle: RePEc:spr:ijsaem:v:8:y:2017:i:3:d:10.1007_s13198-017-0577-9
    DOI: 10.1007/s13198-017-0577-9
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    References listed on IDEAS

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    1. Pourgol-Mohamad, Mohammad & Mosleh, Ali & Modarres, Mohammad, 2010. "Methodology for the use of experimental data to enhance model output uncertainty assessment in thermal hydraulics codes," Reliability Engineering and System Safety, Elsevier, vol. 95(2), pages 77-86.
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    Cited by:

    1. Ajey Shakti Mishra & Upendra Kumar Acharya & Akanksha Srivastava & Aashi Rohit Modi & Sandeep Kumar, 2024. "Brain tumor image segmentation using model average ensembling of deep networks," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 15(8), pages 3915-3925, August.
    2. Arezoo Amirpourabasi & Mohammad Pourgol-Mohammad & Hanieh Niroomand-Oscuii, 2017. "Reliability Evaluation for Biomedical Systems: Case Study of a Biological Cell Freezing," Current Trends in Biomedical Engineering & Biosciences, Juniper Publishers Inc., vol. 6(3), pages 45-52, July.

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