Harshad Sakariya, Ravi Shankar Prasad, Sushil Kumar
{"title":"A study on brain tumor dynamics in two-dimensional irregular domain with variable-order time-fractional derivative","authors":"Harshad Sakariya, Ravi Shankar Prasad, Sushil Kumar","doi":"10.1016/j.cmpb.2025.108700","DOIUrl":null,"url":null,"abstract":"<div><h3>Background and Objective:</h3><div>Understanding tumor growth in the brain is a crucial and complex challenge. This study aims to develop and analyze a brain tumor growth model that incorporates variable-order time-fractional derivatives within a two-dimensional irregular domain. The purpose is to explore the effects of time-fractional orders, mutation rates, and growth parameters on tumor dynamics.</div></div><div><h3>Methods:</h3><div>The model employs the finite difference method for temporal discretization and Gaussian radial basis functions based on Kansa’s method for spatial variables. Ulam–Hyers stability analysis is performed to ensure the model’s stability and the existence and uniqueness of the solution are established. Additionally, the stability and convergence of the scheme are analyzed. Code verification is conducted to confirm the accuracy and reliability of the computational approach. Key parameters, such as the mutation rate <span><math><msub><mrow><mi>K</mi></mrow><mrow><mi>c</mi></mrow></msub></math></span> and growth parameters <span><math><msub><mrow><mi>σ</mi></mrow><mrow><mn>1</mn></mrow></msub></math></span> and <span><math><msub><mrow><mi>σ</mi></mrow><mrow><mn>2</mn></mrow></msub></math></span>, are investigated under various time-fractional derivative orders, including variable orders.</div></div><div><h3>Results:</h3><div>The numerical simulations provide a detailed analysis of tumor cell dynamics, accounting for heterogeneity and fractional effects. Graphical representations reveal novel behaviors induced by variable-order time-fractional derivatives, including their impact on tumor cell population growth. Changes in the mutation rate and growth parameters significantly influence the results, demonstrating sensitivity to parameter variations.</div></div><div><h3>Conclusions:</h3><div>This study demonstrates that the integration of variable-order time-fractional derivatives into brain tumor models introduces memory effects, revealing new insights into tumor behavior. The findings highlight the importance of fractional-order parameters in accurately modeling brain tumor growth, which could have potential implications for predicting tumor progression and developing targeted treatments.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"264 ","pages":"Article 108700"},"PeriodicalIF":4.9000,"publicationDate":"2025-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computer methods and programs in biomedicine","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0169260725001178","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
引用次数: 0
Abstract
Background and Objective:
Understanding tumor growth in the brain is a crucial and complex challenge. This study aims to develop and analyze a brain tumor growth model that incorporates variable-order time-fractional derivatives within a two-dimensional irregular domain. The purpose is to explore the effects of time-fractional orders, mutation rates, and growth parameters on tumor dynamics.
Methods:
The model employs the finite difference method for temporal discretization and Gaussian radial basis functions based on Kansa’s method for spatial variables. Ulam–Hyers stability analysis is performed to ensure the model’s stability and the existence and uniqueness of the solution are established. Additionally, the stability and convergence of the scheme are analyzed. Code verification is conducted to confirm the accuracy and reliability of the computational approach. Key parameters, such as the mutation rate and growth parameters and , are investigated under various time-fractional derivative orders, including variable orders.
Results:
The numerical simulations provide a detailed analysis of tumor cell dynamics, accounting for heterogeneity and fractional effects. Graphical representations reveal novel behaviors induced by variable-order time-fractional derivatives, including their impact on tumor cell population growth. Changes in the mutation rate and growth parameters significantly influence the results, demonstrating sensitivity to parameter variations.
Conclusions:
This study demonstrates that the integration of variable-order time-fractional derivatives into brain tumor models introduces memory effects, revealing new insights into tumor behavior. The findings highlight the importance of fractional-order parameters in accurately modeling brain tumor growth, which could have potential implications for predicting tumor progression and developing targeted treatments.
背景与目的:了解脑肿瘤的生长是一项关键而复杂的挑战。本研究旨在开发和分析一种脑肿瘤生长模型,该模型在二维不规则域中加入了可变阶次的时间分数导数。方法:该模型采用有限差分法进行时间离散化,采用基于 Kansa 方法的高斯径向基函数进行空间变量离散化。为确保模型的稳定性,进行了 Ulam-Hyers 稳定性分析,并确定了解的存在性和唯一性。此外,还分析了方案的稳定性和收敛性。还进行了代码验证,以确认计算方法的准确性和可靠性。结果:数值模拟提供了肿瘤细胞动力学的详细分析,考虑了异质性和分数效应。图形显示了变阶时间分数导数诱发的新行为,包括其对肿瘤细胞群体增长的影响。突变率和生长参数的变化对结果有显著影响,表明了对参数变化的敏感性。结论:本研究表明,将变阶时间分数导数整合到脑肿瘤模型中会引入记忆效应,从而揭示肿瘤行为的新见解。研究结果凸显了分数阶参数在准确模拟脑肿瘤生长过程中的重要性,这对预测肿瘤进展和开发靶向治疗具有潜在意义。
期刊介绍:
To encourage the development of formal computing methods, and their application in biomedical research and medical practice, by illustration of fundamental principles in biomedical informatics research; to stimulate basic research into application software design; to report the state of research of biomedical information processing projects; to report new computer methodologies applied in biomedical areas; the eventual distribution of demonstrable software to avoid duplication of effort; to provide a forum for discussion and improvement of existing software; to optimize contact between national organizations and regional user groups by promoting an international exchange of information on formal methods, standards and software in biomedicine.
Computer Methods and Programs in Biomedicine covers computing methodology and software systems derived from computing science for implementation in all aspects of biomedical research and medical practice. It is designed to serve: biochemists; biologists; geneticists; immunologists; neuroscientists; pharmacologists; toxicologists; clinicians; epidemiologists; psychiatrists; psychologists; cardiologists; chemists; (radio)physicists; computer scientists; programmers and systems analysts; biomedical, clinical, electrical and other engineers; teachers of medical informatics and users of educational software.