Logistically Modelling The Growth Rates of Different Cancer Types and Tumors
DOI:
https://doi.org/10.58445/rars.3383Keywords:
Cancer Biology, Cancer PrognosisAbstract
The unpredictability and eccentricity of cancer growth are issues that have not yet been resolved. With cancer being a personalized disease that stems from unique cell mutations, there are multiple types, and some grow faster than others. The purpose of this research project is to discover the most efficient way to track tumor growth using coded simulations and to discover how the growth trajectory can affect the course of treatment. A model was developed (ChatGPT only contributed to coding it) that compiles research from a variety of online medical resources and articles to project the likely growth trajectories for 10 different types of malignant tumors. This paper will discuss how and why the code works and what resources were used to develop accurate results.
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