The Stochastic Mathematical Model Predicts Angio-Therapy Could Delay the Emergence of Metastases in Lung Cancer

Emilia Kozłowska; Andrzej Świerniak

doi:10.1007/978-3-030-83704-4_7

The Stochastic Mathematical Model Predicts Angio-Therapy Could Delay the Emergence of Metastases in Lung Cancer

Emilia Kozłowska
, Andrzej Świerniak

Department of Systems Biology and Engineering (RAU/RAU1)

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

3 Citations (Scopus)

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.

Original language	English
Title of host publication	Biocybernetics and Biomedical Engineering – Current Trends and Challenges - Proceedings of the 22nd Polish Conference on Biocybernetics and Biomedical Engineering
Editors	Dorota G. Pijanowska, Krzysztof Zieliński, Adam Liebert, Janusz Kacprzyk
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	64-73
Number of pages	10
ISBN (Print)	9783030837037
DOIs	https://doi.org/10.1007/978-3-030-83704-4_7
Publication status	Published - 2022
Event	22nd Polish Conference on Biocybernetics and Biomedical Engineering, PCBBE 2021 - Warsaw, Poland Duration: 19 May 2021 → 21 May 2021

Publication series

Name	Lecture Notes in Networks and Systems
Volume	293
ISSN (Print)	2367-3370
ISSN (Electronic)	2367-3389

Conference

Conference	22nd Polish Conference on Biocybernetics and Biomedical Engineering, PCBBE 2021
Country/Territory	Poland
City	Warsaw
Period	19/05/21 → 21/05/21

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

Angio-therapy
Lung cancer
Metastases
Stochastic differential equations

ASJC Scopus subject areas

Control and Systems Engineering
Signal Processing
Computer Networks and Communications

Access to Document

10.1007/978-3-030-83704-4_7

Cite this

Kozłowska, E., & Świerniak, A. (2022). The Stochastic Mathematical Model Predicts Angio-Therapy Could Delay the Emergence of Metastases in Lung Cancer. In D. G. Pijanowska, K. Zieliński, A. Liebert, & J. Kacprzyk (Eds.), Biocybernetics and Biomedical Engineering – Current Trends and Challenges - Proceedings of the 22nd Polish Conference on Biocybernetics and Biomedical Engineering (pp. 64-73). (Lecture Notes in Networks and Systems; Vol. 293). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-83704-4_7

Kozłowska, Emilia ; Świerniak, Andrzej. / The Stochastic Mathematical Model Predicts Angio-Therapy Could Delay the Emergence of Metastases in Lung Cancer. Biocybernetics and Biomedical Engineering – Current Trends and Challenges - Proceedings of the 22nd Polish Conference on Biocybernetics and Biomedical Engineering. editor / Dorota G. Pijanowska ; Krzysztof Zieliński ; Adam Liebert ; Janusz Kacprzyk. Springer Science and Business Media Deutschland GmbH, 2022. pp. 64-73 (Lecture Notes in Networks and Systems).

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title = "The Stochastic Mathematical Model Predicts Angio-Therapy Could Delay the Emergence of Metastases in Lung Cancer",

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.",

keywords = "Angio-therapy, Lung cancer, Metastases, Stochastic differential equations",

author = "Emilia Koz{\l}owska and Andrzej {\'S}wierniak",

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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 DG Pijanowska, K Zieliński, A Liebert & J Kacprzyk (eds), Biocybernetics and Biomedical Engineering – Current Trends and Challenges - Proceedings of the 22nd Polish Conference on Biocybernetics and Biomedical Engineering. Lecture Notes in Networks and Systems, vol. 293, Springer Science and Business Media Deutschland GmbH, pp. 64-73, 22nd Polish Conference on Biocybernetics and Biomedical Engineering, PCBBE 2021, Warsaw, Poland, 19/05/21. https://doi.org/10.1007/978-3-030-83704-4_7

The Stochastic Mathematical Model Predicts Angio-Therapy Could Delay the Emergence of Metastases in Lung Cancer. / Kozłowska, Emilia; Świerniak, Andrzej.
Biocybernetics and Biomedical Engineering – Current Trends and Challenges - Proceedings of the 22nd Polish Conference on Biocybernetics and Biomedical Engineering. ed. / Dorota G. Pijanowska; Krzysztof Zieliński; Adam Liebert; Janusz Kacprzyk. Springer Science and Business Media Deutschland GmbH, 2022. p. 64-73 (Lecture Notes in Networks and Systems; Vol. 293).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - The Stochastic Mathematical Model Predicts Angio-Therapy Could Delay the Emergence of Metastases in Lung Cancer

AU - Kozłowska, Emilia

AU - Świerniak, Andrzej

PY - 2022

Y1 - 2022

N2 - 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.

AB - 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.

KW - Angio-therapy

KW - Lung cancer

KW - Metastases

KW - Stochastic differential equations

UR - https://www.scopus.com/pages/publications/85115661048

U2 - 10.1007/978-3-030-83704-4_7

DO - 10.1007/978-3-030-83704-4_7

M3 - Conference contribution

AN - SCOPUS:85115661048

SN - 9783030837037

T3 - Lecture Notes in Networks and Systems

SP - 64

EP - 73

BT - Biocybernetics and Biomedical Engineering – Current Trends and Challenges - Proceedings of the 22nd Polish Conference on Biocybernetics and Biomedical Engineering

A2 - Pijanowska, Dorota G.

A2 - Zieliński, Krzysztof

A2 - Liebert, Adam

A2 - Kacprzyk, Janusz

PB - Springer Science and Business Media Deutschland GmbH

T2 - 22nd Polish Conference on Biocybernetics and Biomedical Engineering, PCBBE 2021

Y2 - 19 May 2021 through 21 May 2021

ER -

Kozłowska E, Świerniak A. The Stochastic Mathematical Model Predicts Angio-Therapy Could Delay the Emergence of Metastases in Lung Cancer. In Pijanowska DG, Zieliński K, Liebert A, Kacprzyk J, editors, Biocybernetics and Biomedical Engineering – Current Trends and Challenges - Proceedings of the 22nd Polish Conference on Biocybernetics and Biomedical Engineering. Springer Science and Business Media Deutschland GmbH. 2022. p. 64-73. (Lecture Notes in Networks and Systems). doi: 10.1007/978-3-030-83704-4_7

The Stochastic Mathematical Model Predicts Angio-Therapy Could Delay the Emergence of Metastases in Lung Cancer

Abstract

Publication series

Conference

UN SDGs

Keywords

ASJC Scopus subject areas

Access to Document

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