Complexity of Brain Tumors

TL;DR

This paper uses scaling analysis and complex visibility networks to characterize and differentiate the growth dynamics of malignant gliomas and benign brain tumors, revealing distinct behaviors aligned with theoretical models.

Contribution

It introduces a novel application of scaling analysis and visibility networks to study tumor growth, providing new descriptors for tumor complexity and behavior.

Findings

Gliomas follow the Family-Vicsek growth ansatz.

Benign tumors exhibit different growth properties.

Visibility networks support the differentiation of tumor types.

Abstract

Tumor growth is a complex process characterized by uncontrolled cell proliferation and invasion of neighboring tissues. The understanding of these phenomena is of vital importance to establish appropriate diagnosis and therapeutic strategy and starts with the evaluation of their complexity with suitable descriptors, such as those produced by scaling analysis. In the present work, scaling analysis is used for the extraction of dynamic parameters that characterize tumor growth processes in brain tumors. The emphasis in the analysis is on the assessment of general properties of tumor growth, such as the Family-Vicsek ansatz, which includes a great variety of ballistic growth models. Results indicate in a definitive way that gliomas strictly behave as it is proposed by the ansatz, while benign tumors behave quite differently. As a complementary view, complex visibility networks derived from the tumor interface support these results and its use is introduced as a possible descriptor in the understanding of tumor growth dynamics.