Tracking parameter changes of an Impulsive Tumor Growth Model

Personalization of chemotherapy requires a mathematical model tailored to the patient. The tailoring requires the knowledge of the parameters specific to the patient, however, these parameters change during the treatment. We used several estimation strategies to identify the parameters of a fourth-order tumor growth model using experimental time series. Estimations were carried out on both the raw time series and also a smoothed variant of the data. We also considered the case where the model parameters change their values at certain time instances due to the physiological behavior of the tumor cells. We found that the accuracy of the model can be increased by letting the parameters change at specific time instants.