Spatial Mapping of Glioblastoma Infiltration: Diffusion Tensor Imaging-Based...

Paper

Abstract

Glioblastoma (GBM) often exhibits distinct anatomical patterns of relapse after radiotherapy. Tumour cell migration along myelinated white matter tracts is a key driver of disease progression. The failure of conventional imaging to capture subclinical infiltration has driven interest in advanced imaging biomarkers capable of quantifying tumour–brain interactions. Diffusion tensor imaging (DTI), radiomics, and connectomics represent a triad of innovative, non-invasive approaches that map white matter architecture, predict recurrence risk, and inform biologically guided treatment strategies. This review examines the biological rationale and clinical applications of DTI-based metrics, radiomic signatures, and tractography-informed connectomics in GBM. We discuss the integration of these modalities into machine learning frameworks and radiotherapy/surgical planning, supported by landmark studies and multi-institutional data. The implications for personalised neuro-oncology are profound, marking a shift towards risk-adaptive, tract-aware treatment strategies that may improve local control and preserve neurocognitive function.

Analysis

Paper intelligence

Summary

This review explores the integration of Diffusion Tensor Imaging (DTI), radiomics, and connectomics to predict glioblastoma (GBM) recurrence by mapping tumour infiltration along white matter tracts.

Key findings

Tumour cell migration along white matter tracts drives GBM progression.
Advanced imaging biomarkers can quantify tumour-brain interactions.
DTI, radiomics, and connectomics offer non-invasive approaches for GBM analysis.
Integration with machine learning can inform treatment strategies.

Methods

Diffusion Tensor Imaging (DTI)
Radiomics
Connectomics
Tractography
Machine learning frameworks

Limitations

Failure of conventional imaging to capture subclinical infiltration.

Applications

Predicting GBM recurrence risk
Informing radiotherapy planning
Informing surgical planning
Personalized neuro-oncology

Confidence100%

Discovery

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Source record

Publication metadata

Authors: Kevin Jang; Michael Back
Published: May 27, 2025
Journal or venue: Brain Sciences
DOI: 10.3390/brainsci15060576
Paper type: journal article
Source: Crossref

DOI