Time series forecasting using ARIMA for modeling of glioma growth in response to radiotherapy

Time series forecasting using ARIMA for modeling of glioma growth in response to radiotherapy

Authors

DOI:

https://doi.org/10.5433/1679-0375.2021v42n1p3

Keywords:

Mathematical models, Tumor growth, Glioblastoma, Time series forecast, Radiotherapy

Abstract

In present days, the growing number of people suffering from cancer has been a major cause for concern worldwide. Glioblastoma in particular, are primary tumors in glial cells located in the central nervous system. Because of this sensitive location, mathematical models have been studied and developed as alternative tools for analyzing tumor growth rates, assisting on the decision-making process for treatment dosage, without exposing the patient’s life. This paper presents two time series models to estimate the growth rate of glioblastoma in response to ionizing radiotherapy treatment. The results obtained indicate that the proposed time series methods attain predictions with a Mean Absolute Percentual Error (MAPE) of approximately 1% to 4%, and simulations show that the Autoregressive Integrated Moving Average (ARIMA) method surpasses the Holt method based on the Mean Square Error (MSE) and MAPE values obtained. Furthermore, the results show that the time series method is applicable to data from two different mathematical models for glioblastoma growth.

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Author Biographies

Larissa Miguez da Silva, Universidade Federal Fluminense - UFF

PhD student at the National Laboratory of Scientific Computing - LNCC.

Gustavo Benitez Alvarez, Universidade Federal Fluminense - UFF

PhD in Nuclear Engineering from the Federal University of Rio de Janeiro. Associate Professor at the Department of Exact Sciences at UFF

Eliane da Silva Christo, Universidade Federal Fluminense - UFF

PhD in Electrical Engineering from the Pontifical Catholic University of Rio de Janeiro. Professor and coordinator of the Professional Master's Program in Production Engineering at Universidade Federal Fluminense

Gerardo Amado Pelén Sierra, Universidade Federal Fluminense - UFF

Master in Computational Modeling in Science and Technology, Federal Fluminense University, Volta Redonda, RJ, Brazil,

Vanessa da Silva Garcia, Universidade Federal Fluminense - UFF

PhD in Nuclear Engineering from the Nuclear Engineering Program (UFRJ). Postdoctoral degree in Computational Modeling by the Postgraduate Program in Computational Modeling in Science and Technology (MCCT / EEIMVR / UFF). Professor at UFF

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Published

2021-04-07

How to Cite

Silva, L. M. da, Alvarez, G. B., Christo, E. da S., Pelén Sierra, G. A., & Garcia, V. da S. (2021). Time series forecasting using ARIMA for modeling of glioma growth in response to radiotherapy. Semina: Ciências Exatas E Tecnológicas, 42(1), 3–12. https://doi.org/10.5433/1679-0375.2021v42n1p3

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