Abstract
Lung cancer is the most common type of cancer across the world. There are known two main types of lung cancer non-small cell lung (NSCLC) and small cell one (SCLC). Here, we focus on NSCLC that is known to disseminate to distant organs such as the brain or bones. What is more, the death of a patient with this type of cancer is caused usually by metastases. It justifies tackling the problem of the emergence of metastases in NSLC in this paper. Here, we investigated which mathematical parameters affect the emergence of metastases using the mathematical oncology approach. We developed a mathematical model of lung cancer growth and metastasis dissemination by taking into account stochasticity in the process. We incorporated into the model two paths of metastasis dissemination through lymphatic and blood vessels. We performed a global sensitivity analysis to identify parameters that affect the model output (metastasis-free survival) the most. We discover that four model parameters have dominating effect on the output and two of them are controllable - the growth rate of primary tumor cells (through the administration of chemotherapeutic agents) and carrying capacity (through the administration of angio-therapy). In conclusion, a mathematical oncology approach could be applied to investigate which treatment modalities in cancer could delay the emergence of metastases.
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Acknowledgements
We thank Professor Rafał Suwiński from the National Cancer Institute in Poland, Gliwice Branch for fruitful discussions and assistance in the clinical part of the project. We would like to acknowledge the financial support of the National Science Center, Poland - grant number 2020/37/B/ST6/01959 (AS, EK) and Foundation for Polish Science (FNP) - scholarship START (EK).
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Kozłowska, E., Świerniak, A. (2022). The Stochastic Mathematical Model Predicts Angio-Therapy Could Delay the Emergence of Metastases in Lung Cancer. In: Pijanowska, D.G., Zieliński, K., Liebert, A., Kacprzyk, J. (eds) Biocybernetics and Biomedical Engineering – Current Trends and Challenges. Lecture Notes in Networks and Systems, vol 293. Springer, Cham. https://doi.org/10.1007/978-3-030-83704-4_7
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DOI: https://doi.org/10.1007/978-3-030-83704-4_7
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