Non-Parametric Modeling of Tumor Growth in Mice Using Real Data


Tejas Sachdeva, Undergraduate


Alona Kryshchenko, Mathematics




According to the Centers for Disease Control, “Cancer is the second leading cause of death in the United States, exceeded only by heart disease. One of every four deaths in the United States is due to cancer.” In an effort to better understand cancer growth we intend to use data gathered from experiments done on mice that were infected with cancer cells. The lung and breast tumor data were collected in labs on mice and extracted from figures in the paper, “Classical Mathematical Models for Description and Prediction of Experimental Tumor Growth” by Sébastien Benzekry et al. using the data extraction tools Data Thief and WebPlotDigitizer. The goal of this project was to estimate the parameters of the exponential-linear model using a nonparametric maximum likelihood estimation method. These parameters can further be used in the exponential-linear model to infer the volume of the tumor at a given time. We utilized tools in the Python platform to implement the model, obtain estimates of the parameters and graphically display the results.

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