https://doi.org/10.1140/epjp/s13360-020-00853-3
Regular Article
Modeling breast tumor growth by a randomized logistic model: A computational approach to treat uncertainties via probability densities
Instituto Universitario de Matemática Multidisciplinar, Universitat Politècnica de València, Valencia, Spain
Received:
27
July
2020
Accepted:
9
October
2020
Published online:
14
October
2020
We consider a randomized discrete logistic equation to describe the dynamics of breast tumor volume. We propose a method, that takes advantage of the principle of maximum entropy, to assign reliable distributions to model inputs (initial condition and coefficients) and sample data, respectively. Since the distributions of coefficients depend on certain parameters, we design a computational procedure to determine the above-mentioned parameters using the information of the probabilistic distributions. The proposed method is successfully applied to model the breast tumor volume using real data. The approach seems to be flexible enough to be adapted to other stochastic models in future contributions.
© Società Italiana di Fisica and Springer-Verlag GmbH Germany, part of Springer Nature 2020
