Mathematical oncology

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

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]

• 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

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