Draft:Mathematical oncology

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Mathematical Oncology is a specialized branch of oncology.:[1] in which mathematical methods, including modeling[2] and simulations,[3] are applied to the study of cancer[4] growth, progression, and treatment.[5] Researchers develop models that describe tumor dynamics, treatment responses, and potential outcomes, supporting the development of more effective treatment strategies.[6] Simulation of cancer behavior potentially reduces the need for early-phase experimental trials.[7][8]

Mathematical oncology employs both deterministic[9] and stochastic[10] models to simulate tumor behavior. These models frequently rely on ordinary differential equations (ODEs)[11] and partial differential equations (PDEs)[12] to represent tumor growth, angiogenesis,[13] metastasis development,[14] and treatment responses.

Control theory[15] and optimization[16] are applied to treatment planning in cancer therapies, particularly in radiotherapy[17] and chemotherapy.[18] By optimizing dose schedules and timing, mathematical oncology aims to maximize therapeutic efficacy while minimizing adverse effects.[19]

Statistical methods[20] can be important for understanding cancer progression, analyzing treatment outcomes, and identifying significant trends in large data sets.[21] Recent advances in artificial intelligence (AI)[22] and machine learning[23] have further impacted the field. AI algorithms[24] can process larger amounts of patient data and identify patterns that may predict individual responses to treatment, personalizing therapeutic strategies.[25]

Recent advancements in computational techniques, particularly in AI, have significantly increased progress in mathematical oncology.[26] 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.[27] [28]

References

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Moffitt Cancer Center's Integrated Mathematical Oncology Program: https://www.moffitt.org/research-science/divisions-and-departments/quantitative-science/integrated-mathematical-oncology/

City of Hope's Division of Mathematical Oncology: https://www.cityofhope.org/research/mathematical-oncology

Society for Mathematical Biology: https://www.smb.org/

Mathematical Oncology Blog: https://mathematical-oncology.org/

  1. ^ "Oncology".
  2. ^ "Mathematical model".
  3. ^ "Simulation".
  4. ^ "Cancer".
  5. ^ Altrock, P., Liu, L. & Michor, F. The mathematics of cancer: integrating quantitative models. Nat Rev Cancer 15, 730–745 (2015). https://doi.org/10.1038/nrc4029
  6. ^ Gibin G. Powathil, Maciej Swat, Mark A.J. Chaplain, Systems oncology: Towards patient-specific treatment regimes informed by multiscale mathematical modeling, Seminars in Cancer Biology, Volume 30, 2015, Pages 13-20, ISSN 1044-579X, https://doi.org/10.1016/j.semcancer.2014.02.003.
  7. ^ "Phases of clinical research".
  8. ^ Chambers RB. The role of mathematical modeling in medical research: "research without patients?". Ochsner J. 2000 Oct;2(4):218-23. PMID: 21765699; PMCID: PMC3117507
  9. ^ "Deterministic system".
  10. ^ "Stochastic process".
  11. ^ "Ordinary differential equation".
  12. ^ "Partial differential equation".
  13. ^ "Angiogenesis".
  14. ^ "Metastasis".
  15. ^ "Control theory".
  16. ^ https://en.wikipedia.org/wiki/Mathematical_optimiza,tion
  17. ^ "Radiation therapy".
  18. ^ https://en.wikipedia.org/wiki/Chemotherap
  19. ^ Optimizing the future: how mathematical models inform treatment schedules for cancer Mathur, Deepti et al. Trends in Cancer, Volume 8, Issue 6, 506 - 516
  20. ^ "Statistical Methods in Medical Research".
  21. ^ "Data set".
  22. ^ "Artificial intelligence".
  23. ^ "Machine learning".
  24. ^ "Artificial intelligence in healthcare".
  25. ^ Janina Hesse, Nina Nelson, Angela Relógio, Shaping the future of precision oncology: Integrating circadian medicine and mathematical models for personalized cancer treatment, Current Opinion in Systems Biology, Volume 37,2024,100506,ISSN 2452-3100, https://doi.org/10.1016/j.coisb.2024.100506
  26. ^ Shimizu H, Nakayama KI. Artificial intelligence in oncology. Cancer Sci. 2020; 111: 1452–1460. https://doi.org/10.1111/cas.14377
  27. ^ El Naqa, I., Karolak, A., Luo, Y. et al. Translation of AI into oncology clinical practice. Oncogene 42, 3089–3097 (2023). https://doi.org/10.1038/s41388-023-02826-z
  28. ^ "AI and Cancer - NCI". 30 May 2024.