Mathematical modelling approaches have become increasingly abundant in cancer
research. The complexity of cancer is well suited to quantitative approaches as it provides …

Increasing knowledge of intertumor heterogeneity, intratumor heterogeneity, and cancer
evolution has improved the understanding of anticancer treatment resistance. A better …

Sex differences in the incidence and outcome of human disease are broadly recognized but,
in most cases, not sufficiently understood to enable sex-specific approaches to treatment …

Despite internal complexity, tumor growth kinetics follow relatively simple laws that can be
expressed as mathematical models. To explore this further, quantitative analysis of the most …

Digital twins employ mathematical and computational models to virtually represent a
physical object (eg, planes and human organs), predict the behavior of the object, and …

Abstract High-grade glioma (HGG), and particularly Glioblastoma (GBM), can exhibit
pronounced intratumoral heterogeneity that confounds clinical diagnosis and management …

In this paper, we discuss how the clustering analysis technique can be applied to analyze
functional magnetic resonance imaging (fMRI) time-series data in the context of …

Using mathematical models to simulate dynamic biological processes has a long history.
Over the past couple of decades or so, quantitative approaches have also made their way …

We develop a methodology to create data-driven predictive digital twins for optimal risk-
aware clinical decision-making. We illustrate the methodology as an enabler for an …

Background Glioblastomas are primary malignant brain tumors with a dismal prognosis.
Knowledge of growth rates and underlying growth dynamics is useful for understanding …