Mathematical oncology
Mathematical oncology is the use of modeling and simulations applied to the study of cancer (oncology).[1]
History
[edit]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
[edit]Modeling types:[4]
- epidemiological data[4]
- mechanistical: tumor growth conceptualized from conceptualization of the tumor matter as a type of mechanism[4]
- cancer cell population evolution[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
[edit]Simulation of cancer behavior potentially reduces the need for early-phase experimental trials.[9][10]
Treatment/therapy
[edit]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
[edit]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
[edit]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
[edit]References
[edit]- ^ a b 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.
- ^ 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.}}
- ^ 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.
- ^ a b c d e 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.
- ^ 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.
- ^ 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.
- ^ 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.
- ^ 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.
- ^ "Phases of Clinical Trials". MD Anderson Cancer Center. University of Texas.
- ^ 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.
- ^ 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.
- ^ 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.
- ^ 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.
- ^ 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.
- ^ 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.
- ^ 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.
- ^ 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.
- ^ 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.
- ^ "AI and Cancer". U.S. Department of Health and Human Services: National Cancer Institute. 30 May 2024.
External links
[edit]Mathematical oncology
[edit]- Moffitt Cancer Center's Integrated Mathematical Oncology Program
- mathematical-oncology.org
- Cook, P. J.; Doll, Richard; Fellingham, S. A. (15 January 1969). "A mathematical model for the age distribution of cancer in man". International Journal of Cancer. 4 (1): 93–112. doi:10.1002/ijc.2910040113. PMID 5346480.
- Boeryd, B.; Ganelius, T.; Lundin, P.; Mellgren, J. (15 September 1966). "Counting and sizing of tumor metastases in experimental oncology". International Journal of Cancer. 1 (5): 497–502. doi:10.1002/ijc.2910010509. PMID 5912537.
- Iversen, S (December 1954). "Human Cancer and Age". British Journal of Cancer. 8 (4): 575–584. doi:10.1038/bjc.1954.62. PMC 2007970. PMID 14351598.
- Arley, Niels; Iversen, Simon (September 1953). "On the Mechanism of Experimental Carcinogenesis: IX. Application of the Hit Theory to Tumours Produced by Ultraviolet Radiation". Acta Pathologica et Microbiologica Scandinavica. 33 (2): 133–150. doi:10.1111/j.1699-0463.1953.tb01503.x. PMID 13123901.