PreprintArticleVersion 1This version is not peer-reviewed
Biomathematics in Cancer Research: Looking into How Mathematical Models Are Used to Understand Tumor Growth and the Effectiveness of Different Treatment Strategies
Version 1
: Received: 29 September 2024 / Approved: 30 September 2024 / Online: 30 September 2024 (14:29:55 CEST)
How to cite:
Olushola, A.; Alao, V. Biomathematics in Cancer Research: Looking into How Mathematical Models Are Used to Understand Tumor Growth and the Effectiveness of Different Treatment Strategies. Preprints2024, 2024092425. https://doi.org/10.20944/preprints202409.2425.v1
Olushola, A.; Alao, V. Biomathematics in Cancer Research: Looking into How Mathematical Models Are Used to Understand Tumor Growth and the Effectiveness of Different Treatment Strategies. Preprints 2024, 2024092425. https://doi.org/10.20944/preprints202409.2425.v1
Olushola, A.; Alao, V. Biomathematics in Cancer Research: Looking into How Mathematical Models Are Used to Understand Tumor Growth and the Effectiveness of Different Treatment Strategies. Preprints2024, 2024092425. https://doi.org/10.20944/preprints202409.2425.v1
APA Style
Olushola, A., & Alao, V. (2024). Biomathematics in Cancer Research: Looking into How Mathematical Models Are Used to Understand Tumor Growth and the Effectiveness of Different Treatment Strategies. Preprints. https://doi.org/10.20944/preprints202409.2425.v1
Chicago/Turabian Style
Olushola, A. and Victoria Alao. 2024 "Biomathematics in Cancer Research: Looking into How Mathematical Models Are Used to Understand Tumor Growth and the Effectiveness of Different Treatment Strategies" Preprints. https://doi.org/10.20944/preprints202409.2425.v1
Abstract
This paper explores the critical role of biomathematics in cancer research, focusing on how mathematical models enhance our understanding of tumor growth and optimize treatment strategies. It begins with an overview of cancer biology, highlighting the complexities of tumor dynamics and the molecular mechanisms driving growth. The paper then delves into various mathematical modeling approaches, including deterministic, stochastic, and agent-based models, illustrating their applications in predicting tumor behavior and treatment responses. Case studies demonstrate the real-world impact of these models on optimizing chemotherapy, radiation therapy, and immunotherapy. Challenges such as data availability, model complexity, and clinical translation are discussed, alongside future directions in the field, including advances in computational power and personalized medicine. Ultimately, this research emphasizes the transformative potential of biomathematics in improving cancer treatment outcomes and underscores the importance of interdisciplinary collaboration in advancing the field.
Keywords
Biomathematics, Tumor growth, Mathematical models, Cancer treatment optimization, Stochastic models, Personalized medicine
Subject
Computer Science and Mathematics, Applied Mathematics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
