J M Bajkowski, H Piotrzkowska-Wróblewska, B Dyniewicz, C I Bajer
{"title":"Mathematical and numerical tumour development modelling for personalised treatment planning.","authors":"J M Bajkowski, H Piotrzkowska-Wróblewska, B Dyniewicz, C I Bajer","doi":"10.1007/s10237-025-01946-7","DOIUrl":null,"url":null,"abstract":"<p><p>This paper presents a mathematical and numerical framework for modelling and parametrising tumour evolution dynamics to enhance computer-aided diagnosis and personalised treatment. The model comprises six differential equations describing cancer cell and blood vessel concentrations, tissue stiffness, Ki- 67 marker distribution, and the apparent velocity of marker propagation. These equations are coupled through S-functions with adjustable coefficients. An inverse problem approach calibrates the model by fitting adjustable coefficients to patient-specific clinical data, thereby enabling disease progression and treatment response simulations. By integrating historical and prospective patient data supported by machine learning algorithms, this framework holds promise as a robust decision-support tool for optimising therapeutic strategies.</p>","PeriodicalId":489,"journal":{"name":"Biomechanics and Modeling in Mechanobiology","volume":" ","pages":""},"PeriodicalIF":3.0000,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Biomechanics and Modeling in Mechanobiology","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1007/s10237-025-01946-7","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"BIOPHYSICS","Score":null,"Total":0}
引用次数: 0
Abstract
This paper presents a mathematical and numerical framework for modelling and parametrising tumour evolution dynamics to enhance computer-aided diagnosis and personalised treatment. The model comprises six differential equations describing cancer cell and blood vessel concentrations, tissue stiffness, Ki- 67 marker distribution, and the apparent velocity of marker propagation. These equations are coupled through S-functions with adjustable coefficients. An inverse problem approach calibrates the model by fitting adjustable coefficients to patient-specific clinical data, thereby enabling disease progression and treatment response simulations. By integrating historical and prospective patient data supported by machine learning algorithms, this framework holds promise as a robust decision-support tool for optimising therapeutic strategies.
期刊介绍：
Mechanics regulates biological processes at the molecular, cellular, tissue, organ, and organism levels. A goal of this journal is to promote basic and applied research that integrates the expanding knowledge-bases in the allied fields of biomechanics and mechanobiology. Approaches may be experimental, theoretical, or computational; they may address phenomena at the nano, micro, or macrolevels. Of particular interest are investigations that
(1) quantify the mechanical environment in which cells and matrix function in health, disease, or injury,
(2) identify and quantify mechanosensitive responses and their mechanisms,
(3) detail inter-relations between mechanics and biological processes such as growth, remodeling, adaptation, and repair, and
(4) report discoveries that advance therapeutic and diagnostic procedures.
Especially encouraged are analytical and computational models based on solid mechanics, fluid mechanics, or thermomechanics, and their interactions; also encouraged are reports of new experimental methods that expand measurement capabilities and new mathematical methods that facilitate analysis.
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号
