A main goal of mathematical and computational oncology is to develop quantitative tools to
determine the most effective therapies for each individual patient. This involves predicting …

In this review, we present current state-of-the-art developments and challenges in the areas
of thermal therapy, ultrasound tomography, image-guided therapies, ocular drug delivery …

The use of chemotherapy against brain tumors faces various limitations to achieving its
therapeutic effect, due to both the inability of anticancer agents to cross the blood–brain …

Tumor growth models have the potential to model and predict the spatiotemporal evolution
of glioma in individual patients. Infiltration of glioma cells is known to be faster along the …

In this article, we propose a proof of concept for the automatic planning of personalized
radiotherapy for brain tumors. A computational model of glioblastoma growth is combined …

Glioblastoma multiforme (GBM) brings serious physical and psychological pain to GBM
patients, whose survival rate remains not optimistic. Long noncoding RNAs (lncRNAs) have …

We develop a general class of thermodynamically consistent, continuum models based on
mixture theory with phase effects that describe the behavior of a mass of multiple interacting …

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 cannot be understood in isolation from their microenvironment. Tumor and stromal
cells change phenotype based upon biochemical and biophysical inputs from their …

Background Low-grade gliomas cause significant neurological morbidity by brain invasion.
There is no universally accepted objective technique available for detection of enlargement …