Morphometrics have been able to distinguish important features of glioblastoma from magnetic resonance imaging (MRI). Using morphometrics computed on segmentations of various imaging abnormalities, we show that the average and range of lacunarity and fractal dimension values across MRI slices can be prognostic for survival. We look at the repeatability of these metrics to multiple segmentations and how they are impacted by image resolution. We speak to the challenges to overcome before these metrics are included in clinical care, and the insight that they may provide. © 2024. The Author(s), under exclusive license to Springer Nature Switzerland AG.
Citation
Lee Curtin. Fractal-Based Morphometrics of Glioblastoma. Advances in neurobiology. 2024;36:545-555
Mesh Tags
PMID: 38468052
View Full Text
