Although mathematical modeling is a mainstay for industrial and many scientific studies,
such approaches have found little application in neurosurgery. However, the fusion of …

This book explores outcome modeling in cancer from a data-centric perspective to enable a
better understanding of complex treatment response, to guide the design of advanced …

Tumors are a main cause of morbidity and mortality worldwide. Despite the efforts of the
clinical and research communities, little has been achieved in the past decades in terms of …

Glioblastomas are highly malignant brain tumors. Knowledge of growth rates and growth
patterns is useful for understanding tumor biology and planning treatment logistics. Based …

We conducted inter-cellular cytokine correlation and network analysis based upon a
stochastic population dynamics model that comprises five cell types and fifteen signaling …

We present a model aiming at the description of intercellular communication on the invasive
character of gliomas. We start from a previous model of ours based on a cellular automaton …

A typical problem in applied mathematics and science is to estimate the future state of a
dynamical system given its current state. One approach aimed at understanding one or more …

Background: Tumor-infiltrated immune cells compose a significant component of many
cancers. They have been observed to have contradictory impacts on tumors. Although the …

In this paper, using the methods of killing tumors and impulsive differential equations, two
immunotherapy antitumor models for describing therapies of general tumors and advanced …

Glioblastoma multiforme (GBM) is a malignant brain cancer with a tendency to both migrate
and proliferate. We propose modeling GBM with heterogeneity in cell phenotypes using a …