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<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 <i>S</i>-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>

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Mathematical and numerical tumour development modelling for personalised treatment planning

  • 期刊论文

  • 发表日期：

    2025年4月8日

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  • J. M. Bajkowski,
  • H. Piotrzkowska-Wróblewska,
  • B. Dyniewicz,
  • C. I. Bajer

摘要

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.