Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content

A Minimal Model of Cancer Growth, Metastasis and Treatment

  • Conference paper
  • First Online:
Recent Challenges in Intelligent Information and Database Systems (ACIIDS 2022)

Abstract

The paper is concerned with modeling cancer growth, metastasis and response to anticancer treatment in a heterogeneous population of patients. Following a discussion of existing models, multicompartmental models are compared using Kaplan-Meier survival curves. Subsequently, different death conditions are analyzed, leading to the final conclusion that a simple, two-compartmental model describes primary and metastatic tumors well enough but death condition must fine-tuned to available clinical survival curves.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 149.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 199.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Dela Cruz, C.S., Tanoue, L.T., Matthay, R.A.: Lung cancer: epidemiology, etiology, and prevention. Clin. Chest Med. 32(4), 605–644 (2011). https://doi.org/10.1016/j.ccm.2011.09.001

    Article  Google Scholar 

  2. Inamura, K.: Lung cancer: understanding its molecular pathology and the 2015 WHO classification. Front. Oncol. 7, 193 (2017). https://doi.org/10.3389/fonc.2017.00193

    Article  Google Scholar 

  3. Popper, H.H.: Progression and metastasis of lung cancer. Cancer Metastasis Rev. 35(1), 75–91 (2016). https://doi.org/10.1007/s10555-016-9618-0

    Article  Google Scholar 

  4. Ten Haaf, K., van der Aalst, C.M., de Koning, H.J., Kaaks, R., Tammemägi, M.C.: Personalising lung cancer screening: an overview of risk-stratification opportunities and challenges. Int. J. Cancer, 149(2): 250–263 (2021), doi:https://doi.org/10.1002/ijc.33578

  5. Ten Haaf, K., et al.: Risk prediction models for selection of lung cancer screening candidates: a retrospective validation study. PLoS Med. 14(4), e1002277 (2017). https://doi.org/10.1371/journal.pmed.1002277

    Article  Google Scholar 

  6. Yeo, Y., et al.: Individual 5-Year lung cancer risk prediction model in Korea using a nationwide representative database. Cancers (Basel). 13(14), 3496 (2021), doi:https://doi.org/10.3390/cancers13143496

  7. Tufail, A.B., et al.: Deep learning in cancer diagnosis and prognosis prediction: a minireview on challenges, recent trends, and future directions. Comput Math Meth. Med. 2021, 9025470 (2021). https://doi.org/10.1155/2021/9025470

    Article  Google Scholar 

  8. Swierniak, A., Kimmel, M., Smieja, J., Puszynski, K., Psiuk-Maksymowicz, K.: System Engineering Approach to Planing Anticancer Therapies. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-28095-0

  9. Schaettler, H., Ledzewicz, U.: Optimal Control for Mathematical Models of Cancer Therapies. An Application of Geometric Methods, Springer, Cham (2015). https://doi.org/10.1007/978-1-4939-2972-6

  10. Dudley, W.N., Wickham, R., Coombs, N.: An introduction to survival statistics: kaplan-meier analysis. J. Adv. Pract. Oncol. 7(1), 91–100 (2016). https://doi.org/10.6004/jadpro.2016.7.1.8

    Article  Google Scholar 

  11. Bilous, M., et al.: Quantitative mathematical modeling of clinical brain metastasis dynamics in non-small cell lung cancer. Sci. Rep. 9(1), 13018 (2019). https://doi.org/10.1038/s41598-019-49407-3

    Article  Google Scholar 

  12. Iwata, K., Kawasaki, K., Shigesada, N.: A dynamical model for the growth and size distribution of multiple metastatic tumors. J. Theor. Biol. 203(2), 177–186 (2000). https://doi.org/10.1006/jtbi.2000.1075

    Article  Google Scholar 

  13. Swierniak, A., Polanski, A., Smieja, J., Kimmel, M.: Modelling growth of drug resistant cancer populations as the system with positive feedback. Math. Comput. Model. 37(11), 1245–1252 (2003). https://doi.org/10.1016/S0895-7177(03)00134-1

    Article  MATH  Google Scholar 

  14. Hanin, L., Seidel, K., Stoevesandt, D.: A universal model of metastatic cancer, its parametric forms and their identification: what can be learned from site-specific volumes of metastases. J. Math. Biol. 72(6), 1633–1662 (2015). https://doi.org/10.1007/s00285-015-0928-6

    Article  MathSciNet  MATH  Google Scholar 

  15. Serre, R., et al.: Mathematical Modeling of cancer immunotherapy and its synergy with radiotherapy. Cancer Res. 76(17), 4931–4940 (2016). https://doi.org/10.1158/0008-5472.CAN-15-3567

    Article  Google Scholar 

  16. Rhodes, A., Hillen, T.: A mathematical model for the immune-mediated theory of metastasis. J Theor Biol. 482, 109999 (2019). https://doi.org/10.1016/j.jtbi.2019.109999

    Article  MathSciNet  MATH  Google Scholar 

  17. Smieja, J., Psiuk-Maksymowicz, K., Swierniak, A.: A framework for modeling and efficacy evaluation of treatment of cancer with metastasis. In: Pijanowska, D.G., Zieliński, K., Liebert, A., Kacprzyk, J. (eds.) Biocybernetics and Biomedical Engineering – Current Trends and Challenges. LNNS, vol. 293, pp. 88–97. Springer, Cham (2022). https://doi.org/10.1007/978-3-030-83704-4_9

    Chapter  Google Scholar 

  18. Bar, J., et al.: Long-term survival of patients with metastatic non-small-cell lung cancer over five decades. J Oncol. 12, 7836264 (2021). https://doi.org/10.1155/2021/7836264

Download references

Acknowledgements

This work has been supported by the NCN grant DEC-2020/37/B/ST6/01959.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Jaroslaw Smieja .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Smieja, J., Swierniak, A., Kimmel, M. (2022). A Minimal Model of Cancer Growth, Metastasis and Treatment. In: Szczerbicki, E., Wojtkiewicz, K., Nguyen, S.V., Pietranik, M., Krótkiewicz, M. (eds) Recent Challenges in Intelligent Information and Database Systems. ACIIDS 2022. Communications in Computer and Information Science, vol 1716. Springer, Singapore. https://doi.org/10.1007/978-981-19-8234-7_44

Download citation

  • DOI: https://doi.org/10.1007/978-981-19-8234-7_44

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-19-8233-0

  • Online ISBN: 978-981-19-8234-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics