Mathematical oncology

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Mathematical oncology is the use of modeling and simulations applied to the study of cancer (oncology). [1]

Contents

History

Teorell made preliminary efforts to model in a work published 1937 [2] because of the problem of the time a drug injected exists within the body was an unknown. [3] Modelling by epidemiological data originated in 1954. [4]

Modeling

Modeling types: [4]

Models use ordinary differential equations [5] and partial differential equations [6] to represent tumor growth, angiogenesis, [7] metastasis development, [8] and treatment responses.

Simulations

Simulation of cancer behavior potentially reduces the need for early-phase experimental trials. [9] [10]

Treatment/therapy

Researchers develop models that describe tumor dynamics, the effects of treatment, to remedy possible non-optimal treatment responses supporting the development of more effective treatment protocols. [11]

Control theory [12] and optimization are applied to treatment planning in cancer therapies, particularly in radiotherapy and chemotherapy. By optimizing dose schedules and timing, mathematical oncology aims to maximize therapeutic efficacy while minimizing adverse effects. [13]

Statistical methods

Statistical methods can be important for understanding cancer progression, analyzing treatment outcomes, and identifying significant trends in large data sets. [1] Advances in artificial intelligence (AI) [14] and machine learning [15] have further impacted the field. AI algorithms [16] can process larger amounts of patient data and identify patterns that may predict individual responses to treatment, personalizing therapeutic strategies. [17]

Computational-AI

AI allows researchers to predict the behavior of individual cells with greater accuracy by integrating diverse types of patient data. AI-driven models can also identify mathematical equations that more precisely reflect tumor growth dynamics, helping researchers uncover relationships between various biological factors more quickly. [18] [19]

Notes

    References

    1. 1 2 Rockne, Russell C.; Scott, Jacob G. (December 21, 2019). "Introduction to Mathematical Oncology". JCO Clinical Cancer Informatics . 3 (3): 1–4. doi:10.1200/CCI.19.00010. PMC   6752950 . PMID   31026176.
    2. ↑ Teorell, T. (1937). "Kinetics of distribution of substances administered to the body, I: The extravascular modes of administration". Archives Internationales de Pharmacodynamie et de Thérapie. 57: 205–225 – via Digitala Vetenskapliga Arkivet.
    3. ↑ Bellman, R. E.; Jacquez, J. A.; Kalaba, R. (1961). "Mathematical models of Chemotherapy". In Neyman, Jerzy (ed.). Proceedings of the Fourth Berkeley Symposium on Mathematical Statistics and Probability . Berkeley, Los Angeles, London: University of California Press, Cambridge University Press. pp. 57, 65.
    4. 1 2 3 4 5 Wodarz, Dominik; Komarova, Natalia (2014). "Mathematical modeling of tumorigenesis". Dynamics of Cancer: Mathematical Foundations of Oncology. New Jersey, London, Singapore, Beijing, Shanghai, Hong Kong, Taipei, Chennai: World Scientific. p. 19. ISBN   978-9814566384.
    5. ↑ Sachs, R.K.; Hlatky, L.R.; Hahnfeldt, P. (June 2001). "Simple ODE models of tumor growth and anti-angiogenic or radiation treatment". Mathematical and Computer Modelling. 33 (12–13): 1297–1305. doi: 10.1016/S0895-7177(00)00316-2 .
    6. ↑ Mirzaei, N. M.; Tatarova, Z.; Hao, W.; Changizi, N.; Asadpoure, A.; Zervantonakis, I. K.; Hu, Y.; Chang, Y. H.; Shahriyari, L. (2022). "A PDE Model of Breast Tumor Progression in MMTV-PyMT Mice". Journal of Personalized Medicine . 12 (5): 807. doi: 10.3390/jpm12050807 . PMC   9145520 . PMID   35629230.
    7. ↑ Hormuth II, DA; Phillips, C. M.; Wu, C.; Lima, E. A.; Lorenzo, G.; Jha, P. K.; Jarrett, A. M.; Oden, J. T.; Yankeelov, T. E. (2021). "Biologically-Based Mathematical Modeling of Tumor Vasculature and Angiogenesis via Time-Resolved Imaging Data". Cancers . 13 (12): 3008. doi: 10.3390/cancers13123008 . PMC   8234316 . PMID   34208448.
    8. ↑ Franssen, L. C.; Lorenzi, T.; Burgess, A. E.; Chaplain, M. A. (March 22, 2019). "A Mathematical Framework for Modelling the Metastatic Spread of Cancer". Bulletin of Mathematical Biology . 81 (6): 1965–2010. doi:10.1007/s11538-019-00597-x. PMC   6503893 . PMID   30903592.
    9. ↑ "Phases of Clinical Trials". MD Anderson Cancer Center . University of Texas.
    10. ↑ Chambers, RB (October 2000). "The Role of Mathematical Modeling in Medical Research: "Research Without Patients?"". Ochsner Journal . 2 (4): 218–223. PMC   3117507 . PMID   21765699.
    11. ↑ Powathil, Gibin G.; Swat, Maciej; Chaplain, Mark A.J. (February 2015). "Systems oncology: Towards patient-specific treatment regimes informed by multiscale mathematical modelling". Seminars in Cancer Biology . 30: 13–20. doi:10.1016/j.semcancer.2014.02.003. hdl: 10023/7713 . ISSN   1044-579X. PMID   24607841.
    12. ↑ Jarrett, A. M.; Faghihi, D.; Hormuth Da, I. I.; Lima, E. A.; Virostko, J.; Biros, G.; Patt, D.; Yankeelov, T. E. (2020). "Optimal Control Theory for Personalized Therapeutic Regimens in Oncology: Background, History, Challenges, and Opportunities". Journal of Clinical Medicine . 9 (5): 1314. doi: 10.3390/jcm9051314 . PMC   7290915 . PMID   32370195.
    13. ↑ Mathur, D.; Barnett, E.; Scher, H. I.; Xavier, J. B. (2022). "Optimizing the future: how mathematical models inform treatment schedules for cancer". Trends in Cancer . 8 (6): 506–516. doi:10.1016/j.trecan.2022.02.005. PMC   9117454 . PMID   35277375.
    14. ↑ Shimizu, Hideyuki; Nakayama, Keiichi I (March 21, 2020). "Artificial intelligence in oncology". Cancer Science . 111 (5): 1452–1460. doi:10.1111/cas.14377. PMC   7226189 . PMID   32133724.
    15. ↑ Bertsimas, D.; Wiberg, H. (2020). "Machine Learning in Oncology: Methods, Applications, and Challenges". JCO Clinical Cancer Informatics . 4 (4): 885–894. doi:10.1200/CCI.20.00072. PMC   7608565 . PMID   33058693.
    16. ↑ Bajwa, Junaid; Munir, Usman; Nori, Aditya; Williams, Bryan (July 21, 2021). "Artificial intelligence in healthcare: transforming the practice of medicine". Future Healthcare Journal . 8 (2): e188 –e194. doi:10.7861/fhj.2021-0095. PMC   8285156 . PMID   34286183.
    17. ↑ Hesse, Janina; Nelson, Nina; Relógio, Angela (March 2024). "Shaping the future of precision oncology: Integrating circadian medicine and mathematical models for personalized cancer treatment". Current Opinion in Systems Biology . 37. doi: 10.1016/j.coisb.2024.100506 .
    18. ↑ El Naqa, Issam; Karolak, Aleksandra; Luo, Yi; Folio, Les; Tarhini, Ahmad A.; Rollison, Dana; Parodi, Katia (8 September 2023). "Translation of AI into oncology clinical practice". Oncogene . 42 (42): 3089–3097. doi:10.1038/s41388-023-02826-z. PMID   37684407.
    19. ↑ "AI and Cancer". U.S. Department of Health and Human Services: National Cancer Institute. 30 May 2024.

    Mathematical oncology

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