ARIMA Analysis For Glioma Growth After Radiotherapy Treatment
Tamara Cristina Onias1; Eliane Da Silva Christo1; Vanessa da Silva Garcia1; Gustavo Benitez Alvarez2
1 Programa de Pós-Graduação em Engenharia de Produção (PPGEP); 2 Programa de Pós-graduação Acadêmico em Modelagem Computacional em Ciência e Tecnologia - (MCCT)
doi:10.20906/CPS/CILAMCE2017-0086
Resumo
Cancer, generically used for the disorderly proliferation of destructive cells, is still a mysterious disease to the scientific community. This paper will comprehensively address the glioma, brain tumor that arise from glial glands, responsible for protecting brain neurons and other regions of the nervous system. Of the various existing treatment methods, the most used and effective is radiation therapy. Its main goal is to destroy residual cells after removal of the tumor by surgery, in such a way as to delay or prevent recurrence of the tumor. Many studies have been done in the area of computational modeling in order to equalize the growth rate of the tumor after treatments with radiotherapy. It's possible to highlight the continuous model proposed by K. R. Swanson (2008), which consists of a second-order partial differential equation of the diffusive reactive type. Several studies already exist in the academic environment foresee the evolution of the tumor radius for each dose of treatment, estimating the optimal dose. These forecasts can be stipulated through time series that aim to predict future values or study the structure of the series and its relation to other series. There are several methods for structuring these series, such as Exponential Cushion as approached by Jesus et al (2016). And, the Box & Jenkins methods that are based on the definition of an integrated auto regression model and moving averages (ARIMA) that represent the stochastic process generating the time series. This paper will aim to find the ARIMA model more suitable for the prediction of glioma growth that minimizes the possible errors of the series and compare its effectiveness in relation to other methods already used in the bibliography. The computational software for analysis is Minitab. It is expected as a result of this paper, to verify, model and simulate, with a certain level of confidence, the survival rate of glioma progression after radiotherapy procedures from analyzes of the time series.
Palavras-chave: Time Series; ARIMA; Glioma; Radiotherapy