From Fiber Tracts to Tumor Spread: Biophysical Modeling of Butterfly Glioma Growth Using Diffusion Tensor Imaging

TL;DR

This study demonstrates that incorporating diffusion tensor imaging data into biophysical models significantly improves the accuracy of predicting butterfly glioma growth patterns, emphasizing white matter's role in tumor spread.

Contribution

It introduces a biophysical modeling approach that integrates fiber tract information from DTI to better predict glioma growth, a novel application in tumor modeling.

Findings

Including fiber orientation data improves model accuracy.

White matter architecture influences tumor migration patterns.

Enhanced modeling can aid in radiotherapy planning.

Abstract

Butterfly tumors are a distinct class of gliomas that span the corpus callosum, producing a characteristic butterfly-shaped appearance on MRI. The distinctive growth pattern of these tumors highlights how white matter fibers and structural connectivity influence brain tumor cell migration. To investigate this relation, we applied biophysical tumor growth models to a large patient cohort, systematically comparing models that incorporate fiber tract information with those that do not. Our results demonstrate that including fiber orientation data significantly improves model accuracy, particularly for a subset of butterfly tumors. These findings highlight the critical role of white matter architecture in tumor spread and suggest that integrating fiber tract information can enhance the precision of radiotherapy target volume delineation.