University of Hertfordshire

By the same authors

Mathematical Lung Cancer Radiotherapy Model – Computational Simulation and Analysis

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Original languageEnglish
Title of host publication2022 International Conference on System Science and Engineering (ICSSE)
PublisherIEEE
Pages1-6
Number of pages6
ISBN (Print)978-1-6654-8853-2
DOIs
Publication statusPublished - 29 May 2022
Event2022 International Conference on System Science and Engineering (ICSSE) - Taichung, Taiwan
Duration: 26 May 202229 May 2022

Conference

Conference2022 International Conference on System Science and Engineering (ICSSE)
Period26/05/2229/05/22

Abstract

Lung cancer is one of the deadliest diseases in the world. The most effective methods for treating lung cancer are radiotherapy and chemotherapy. In this paper, we focused on the radiotherapy. Now, mathematical models have been extended to many biomedical fields to provide assistance for analysis, evaluation, prediction and optimization. Methods. In this article, we propose a mathematical tumor growth model derived from the classic Gompertz tumor model, and use appropriate parameters to obtain a radiotherapy model. The model was fitted with a number of studies and clinical data through computer simulations, and analyzed the effects of certain doses and α/β values on the effect of radiotherapy, and provided results consistent with the growth of lung cancer cells in vitro. Results. Using optimization technology, the model runs stably. The simulation results show that some radiotherapy doses and α/β values have significant changes in radiotherapy. The proposed mathematical model can provide basic work for the analysis and evaluation of radiotherapy plans. Conclusions. With the support of appropriate parameters, our model can simulate and analyze tumor radiotherapy plans, and provide certain theoretical guidance for the personalized optimization of radiotherapy. It is expected that in the near future, the mathematical models will become valuable tools for optimizing personalized medicine.

ID: 32317087