JCNRJournal of Clinical and Nursing Research2208-36852208-3693Bio-Byword Scientific Publishing Pty. Ltd.10.26689/jcnr.v9i4.10227ArticleParameter Estimation of a Tumor Growth Model under Data-driven Approach and Its Numerical Solution in Matlabhttps://artdesignp.com/journal/JCNR/9/4/10.26689/jcnr.v9i4.10227ChenZhuo,ZengYihan,ChenWei,ZhengRuixian,DuZejun,GeMeibao2025942025-04-28This paper focuses on the numerical solution of a tumor growth model under a data-driven approach. Based on the inherent laws of the data and reasonable assumptions, an ordinary differential equation model for tumor growth is established. Nonlinear fitting is employed to obtain the optimal parameter estimation of the mathematical model, and the numerical solution is carried out using the Matlab software. By comparing the clinical data with the simulation results, a good agreement is achieved, which verifies the rationality and feasibility of the model.

Sheema S, Roberto B, Paolo M, et al., 2016, Mathematical Modeling of Drug Resistance Due to KRAS Mutation in Colorectal Cancer. J Theoret Biol, 389(1): 263–273.

Nitish P, Feba S, Wayne C, et al., 2016, A Three Dimensional Micropatterned Tumor Model for Breast Cancer Cell Migration Studies. Biomaterials, 81(3): 72–83.

Cui S, 2009, The Free Boundary Problem of Tumor Growth. Advances in Mathematics, 38(1): 1–18.

Xu Y, 2004, A Free Boundary Problem Model of Ductal Carcinoma in Situ. Discrete and Continuous Dynamical Systems Series, B4(1): 337–348.

Liu K, Xu Y, Xu D, 2020, Numerical Algorithms for a Free Boundary Problem Model of DCIS and a Related Inverse Problem. Applicable Analysis, 99: 1181–1194.

Ge M, Xu D, 2022, Biparametric Identification for a Free Boundary of Ductal Carcinoma in Situ. Applicable Analysis, DOI:10.1080/00036811.2022.2038786.

Si S, Sun Y, 2021, Algorithms and Applications of Mathematical Modeling (3rd Edition). Beijing: National Defense Industry Press, China.