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Abstract

This scenario guides students in the use of differential equation models to predict cancer growth and optimize treatment outcomes. Several classical models for cancer growth are studied, including exponential, power law, Bertalanffy, logistic, and Gompertz. They examine the behaviors of the equations and solutions through qualitative techniques. Furthermore, students evaluate how cancer treatments affect tumor growth. Real cancer data are provided to give options for data fitting and prediction of cancer growth.

Through step-by-step investigation, conjecturing, predicting, and analyzing, students discover how their knowledge can be used to address complex and real problems, and improve problem solving ability and mathematical reasoning skills.

Cite this work

Researchers should cite this work as follows:

  • Jue Wang (2018), "1-102-S-CancerGrowth," https://www.simiode.org/resources/4846.

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