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Performance of linear and stochastic cell-growth model of Ductal Carcinoma In Situ (DCIS)

According to World Health Organization (WHO), in 2020 there were 2.3 million of women have been diagnosed with breast cancer and there are up to 685,000 deaths globally. 85% of breast cancer arises in the lining cells (epithelium) of the ducts known as ductal carcinoma in situ (DCIS). One of the con...

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Main Authors: Nurul Anis, Abdul Satar, Noor Amalina Nisa, Ariffin, Norhayati, Rosli, Mazma Syahidatul Ayuni, Mazlan
Format: Article
Language:English
Published: Semarak Ilmu Publishing 2025
Subjects:
Q Science (General)
QA Mathematics
Online Access:http://umpir.ump.edu.my/id/eprint/42126/1/Performance%20of%20linear%20and%20stochastic%20cell-growth.pdf
http://umpir.ump.edu.my/id/eprint/42126/
https://doi.org/10.37934/araset.46.2.19
https://doi.org/10.37934/araset.46.2.19
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spelling my.ump.umpir.421262024-09-30T04:49:50Z http://umpir.ump.edu.my/id/eprint/42126/ Performance of linear and stochastic cell-growth model of Ductal Carcinoma In Situ (DCIS) Nurul Anis, Abdul Satar Noor Amalina Nisa, Ariffin Norhayati, Rosli Mazma Syahidatul Ayuni, Mazlan Q Science (General) QA Mathematics According to World Health Organization (WHO), in 2020 there were 2.3 million of women have been diagnosed with breast cancer and there are up to 685,000 deaths globally. 85% of breast cancer arises in the lining cells (epithelium) of the ducts known as ductal carcinoma in situ (DCIS). One of the contributing factors that increase cancer mortality is the lack of understanding of the biological complexities of cancer growth and its evolution. Mathematical-model approaches are widely used to quantitatively understand the behaviour of the cancer cells and the treatment resistance. In order to justify the treatment customization and convey the treatment inefficacy, the mathematical modelling is usually considered as a tool to support drug and treatment decision making. By now, several mathematical models via ordinary differential equations (ODEs) for the cancer cell growth process have been formulated in the literature. Unfortunately, due to the noise behaviour of cancerous cells, the developed linear model cannot represent the real behaviour of the cancer cells growth which led to the development of stochastic model of cancer cells growth. This study is devoted to comparing the performance of linear and stochastic cell growth model of DCIS. The linear model cell growth model of DCIS has been solved via Runge-Kutta method of order 4.0 while the stochastic cell growth model of DCIS has been solved via fifth-stage stochastic Runge-Kutta method (SRK5) of order 2.0. The numerical results obtained have been compared to the real cell growth data for DCIS patients and the best model representing the cell growth of DCIS has been concluded. This study has shown that stochastic Gompertzian model has a great representation of the real systems of breast cancer cell growth. This useful clinical knowledge provides a better understanding of cancer evolution to overcome the treatment resistance hence may help oncologists to design better treatment strategies and bring opportunities to treat cancer patients. Semarak Ilmu Publishing 2025-04 Article PeerReviewed pdf en cc_by_nc_4 http://umpir.ump.edu.my/id/eprint/42126/1/Performance%20of%20linear%20and%20stochastic%20cell-growth.pdf Nurul Anis, Abdul Satar and Noor Amalina Nisa, Ariffin and Norhayati, Rosli and Mazma Syahidatul Ayuni, Mazlan (2025) Performance of linear and stochastic cell-growth model of Ductal Carcinoma In Situ (DCIS). Journal of Advanced Research in Applied Sciences and Engineering Technology, 46 (2). pp. 1-9. ISSN 2462-1943. (Published) https://doi.org/10.37934/araset.46.2.19 https://doi.org/10.37934/araset.46.2.19
institution Universiti Malaysia Pahang Al-Sultan Abdullah
building UMPSA Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Pahang Al-Sultan Abdullah
content_source UMPSA Institutional Repository
url_provider http://umpir.ump.edu.my/
language English
topic Q Science (General)
QA Mathematics
spellingShingle Q Science (General)
QA Mathematics
Nurul Anis, Abdul Satar
Noor Amalina Nisa, Ariffin
Norhayati, Rosli
Mazma Syahidatul Ayuni, Mazlan
Performance of linear and stochastic cell-growth model of Ductal Carcinoma In Situ (DCIS)
description According to World Health Organization (WHO), in 2020 there were 2.3 million of women have been diagnosed with breast cancer and there are up to 685,000 deaths globally. 85% of breast cancer arises in the lining cells (epithelium) of the ducts known as ductal carcinoma in situ (DCIS). One of the contributing factors that increase cancer mortality is the lack of understanding of the biological complexities of cancer growth and its evolution. Mathematical-model approaches are widely used to quantitatively understand the behaviour of the cancer cells and the treatment resistance. In order to justify the treatment customization and convey the treatment inefficacy, the mathematical modelling is usually considered as a tool to support drug and treatment decision making. By now, several mathematical models via ordinary differential equations (ODEs) for the cancer cell growth process have been formulated in the literature. Unfortunately, due to the noise behaviour of cancerous cells, the developed linear model cannot represent the real behaviour of the cancer cells growth which led to the development of stochastic model of cancer cells growth. This study is devoted to comparing the performance of linear and stochastic cell growth model of DCIS. The linear model cell growth model of DCIS has been solved via Runge-Kutta method of order 4.0 while the stochastic cell growth model of DCIS has been solved via fifth-stage stochastic Runge-Kutta method (SRK5) of order 2.0. The numerical results obtained have been compared to the real cell growth data for DCIS patients and the best model representing the cell growth of DCIS has been concluded. This study has shown that stochastic Gompertzian model has a great representation of the real systems of breast cancer cell growth. This useful clinical knowledge provides a better understanding of cancer evolution to overcome the treatment resistance hence may help oncologists to design better treatment strategies and bring opportunities to treat cancer patients.
format Article
author Nurul Anis, Abdul Satar
Noor Amalina Nisa, Ariffin
Norhayati, Rosli
Mazma Syahidatul Ayuni, Mazlan
author_facet Nurul Anis, Abdul Satar
Noor Amalina Nisa, Ariffin
Norhayati, Rosli
Mazma Syahidatul Ayuni, Mazlan
author_sort Nurul Anis, Abdul Satar
title Performance of linear and stochastic cell-growth model of Ductal Carcinoma In Situ (DCIS)
title_short Performance of linear and stochastic cell-growth model of Ductal Carcinoma In Situ (DCIS)
title_full Performance of linear and stochastic cell-growth model of Ductal Carcinoma In Situ (DCIS)
title_fullStr Performance of linear and stochastic cell-growth model of Ductal Carcinoma In Situ (DCIS)
title_full_unstemmed Performance of linear and stochastic cell-growth model of Ductal Carcinoma In Situ (DCIS)
title_sort performance of linear and stochastic cell-growth model of ductal carcinoma in situ (dcis)
publisher Semarak Ilmu Publishing
publishDate 2025
url http://umpir.ump.edu.my/id/eprint/42126/1/Performance%20of%20linear%20and%20stochastic%20cell-growth.pdf
http://umpir.ump.edu.my/id/eprint/42126/
https://doi.org/10.37934/araset.46.2.19
https://doi.org/10.37934/araset.46.2.19
_version_ 1822924688527982592
score 13.244413

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