Modeling and optimal analysis of lung cancer cell growth and apoptosis with fractional-order dynamics

被引:0
作者
Swain, Sumit [1 ]
Swain, Satish [2 ]
Panda, Bandhan [3 ]
Tripathy, Madhab Chandra [1 ]
机构
[1] School of Electronic Sciences, Odisha University of Technology and Research, Bhubaneswar
[2] Department of Infectious Disease, Christian Medical College, Vellore
[3] Department of Computer Science, National Institute of Science and Technology, Berhampur
关键词
Cancer treatment modeling; Fractional-order calculus; Fractional-order logistic equation; Genetic algorithm optimization; Lung cancer cell growth; Tumor dynamics;
D O I
10.1016/j.compbiomed.2025.109837
中图分类号
学科分类号
摘要
This study explores the application of fractional-order calculus in modeling lung cancer cell growth dynamics, emphasizing its advantages over traditional integer-order models. Conventional models often fail to capture the complexities of tumor behavior, such as memory effects and long-range interactions. The fractional-order logistic equation provides a more sophisticated framework that integrates intrinsic growth rates and environmental constraints, enabling a nuanced analysis of tumor progression and treatment responses. A key component of this research involves deriving a Laplace domain representation to assess transfer function characteristics, which aids in evaluating stability and response across various frequency domains. An improved fractional-order model was developed to illustrate the interplay between cancer proliferation and immune response mechanisms. The optimization of critical parameters, including the fractional-order ultimate growth rate, has been achieved using a genetic algorithm (GA) optimization. The main findings of this work include the potential of fractional-order modeling to understand, analyze, and determine treatment strategies, ultimately advancing the understanding of cancer dynamics and improving patient outcomes in oncology. Here, it shows the application of fractional-order dynamics to determine the effective treatment procedure concerning all complex parameters involved. This research contributes to the growing body of knowledge on sophisticated mathematical frameworks in cancer research, facilitating the development of tailored therapeutic interventions based on individual patient profiles. © 2025 Elsevier Ltd
引用
收藏
相关论文
共 35 条
[1]  
Rihan F.A., Kandasamy U., Alsakaji H.J., Sottocornola N., Dynamics of a fractional-order delayed model of COVID-19 with vaccination efficacy, Vaccines, 11, (2023)
[2]  
Mahdy A.M.S., Almalki N., Higazy M., A general fractional breast cancer model: Model graph energy, Caputo–fabrizio derivative existence and uniqueness plus numerical simulation, Partial. Differ. Equations Appl. Math., 10, (2024)
[3]  
Ghita M., Copot D., Ionescu C.M., Lung cancer dynamics using fractional order impedance modeling on a mimicked lung tumor setup, J. Adv. Res., 32, pp. 61-71, (2021)
[4]  
Mohammed M., Analysis of an electronic methods for nasopharyngeal carcinoma: Prevalence, diagnosis, challenges and technologies, J. Comput. Sci., 21, pp. 241-254, (2017)
[5]  
Heuvelmans M.A., Vliegenthart R., de Koning H.J., Groen H.J.M., van Putten M.J.A.M., Yousaf-Khan U., Weenink C., Nackaerts K., de Jong P.A., Oudkerk M., Quantification of growth patterns of screen-detected lung cancers: The NELSON study, Lung Cancer, 108, pp. 48-54, (2017)
[6]  
Surendar P., Ponni Bala M., Diagnosis of lung cancer using hybrid deep neural network with adaptive sine cosine crow search algorithm, J. Comput. Sci., 53, (2021)
[7]  
Ghita M., Billiet C., Copot D., Verellen D., Ionescu C.M., Parameterisation of respiratory impedance in lung cancer patients from forced oscillation lung function test, IEEE Trans. Biomed. Eng., 70, pp. 1587-1598, (2023)
[8]  
Germer S., Rudolph C., Labohm L., Katalinic A., Rath N., Rausch K., Holleczek B., Handels H., Survival analysis for lung cancer patients: A comparison of cox regression and machine learning models, Int. J. Med. Inf., 191, (2024)
[9]  
Predicting the future risk of lung cancer: development, and internal and external validation of the CanPredict (lung) model in 19.67 million people and evaluation of model performance against seven other risk prediction models, Lancet Respir. Med., 11, pp. 685-697, (2023)
[10]  
Magin R.L., Fractional calculus models of complex dynamics in biological tissues, Comput. Math. Appl., 59, pp. 1586-1593, (2010)