Abstract
Low-grade gliomas are infiltrative brain tumors that can lead to significant neurological deficits due to their invasive nature. The prevailing belief is that low-grade gliomas primarily disseminate along white matter tracts, but quantitative in vivo evidence supporting this concept is lacking. Clarifying their true growth patterns is essential for optimizing therapeutic strategies. We conducted a quantitative analysis of tumor growth patterns in a longitudinal cohort of 43 untreated patients with unigyral World Health Organization grade 2 or 3 gliomas, stratified by their anatomical locations within the neocortex, mesocortex and allocortex. Serial MRI scans were used to generate vector deformation fields, providing detailed three-dimensional representations of tumor evolution over time. These vector deformation fields were compared with diffusion tensor imaging data to assess the alignment of tumor growth with white matter pathways. Quantitative analysis revealed that low-grade gliomas do not predominantly expand along white matter tracts. Instead, they remain confined within specific anatomical boundaries, in respect to their topology of origin. Angular measurements and heat map analysis indicated that tumor growth is directed towards the subventricular zone and may follow their respective radial units. These consistent observations across different anatomical regions challenge the traditional model of glioma progression, suggesting that early-stage glioma expansion is closely governed by ontogenetic factors. In conclusion, this study provides the first quantitative evidence that phenotypical low-grade gliomas do not primarily follow white matter tracts but may instead be influenced by ontogenetic mechanisms. These insights necessitate a re-evaluation of existing models of glioma progression and underscore the importance of incorporating developmental aspects into treatment planning to enhance patient outcomes.
Original language | English |
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Article number | fcaf157 |
Number of pages | 16 |
Journal | Brain Communications |
Volume | 7 |
Issue number | 3 |
DOIs | |
Publication status | Published - Apr 2025 |
Fields of science
- 301405 Neuropathology
- 302052 Neurology
- 302051 Neurosurgery
- 301114 Cell biology
- 101018 Statistics
- 101029 Mathematical statistics
- 102009 Computer simulation
- 101026 Time series analysis
- 101024 Probability theory
- 102037 Visualisation
- 303007 Epidemiology
- 303040 Health services research
- 502025 Econometrics
- 504006 Demography
- 305907 Medical statistics
- 504004 Population statistics
- 509013 Social statistics
- 102035 Data science
- 106007 Biostatistics
- 504007 Empirical social research
JKU Focus areas
- Sustainable Development: Responsible Technologies and Management
- Digital Transformation