Shape matters: morphological metrics of glioblastoma imaging abnormalities as biomarkers of prognosis
Year: 2021
Type: article
Study design: Observational Study
Abstract: Lacunarity, a quantitative morphological measure of how shapes fill space, and fractal dimension, a morphological measure of the complexity of pixel arrangement, have shown relationships with outcome across a variety of cancers. However, the application of these metrics to glioblastoma (GBM), a very aggressive primary brain tumor, has not been fully explored. In this project, we computed lacunarity and fractal dimension values for GBM-induced abnormalities on clinically standard magnetic resonance imaging (MRI). In our patient cohort (n = 402), we connect these morphological metrics calculated on pretreatment MRI with the survival of patients with GBM. We calculated lacunarity and fractal dimension on necrotic regions (n = 390), all abnormalities present on T1Gd MRI (n = 402), and abnormalities present on T2/FLAIR MRI (n = 257). We also explored the relationship between these metrics and age at diagnosis, as well as abnormality volume. We found statistically significant relationships to outcome for all three imaging regions that we tested, with the shape of T2/FLAIR abnormalities that are typically associated with edema showing the strongest relationship with overall survival. This link between morphological and survival metrics could be driven by underlying biological phenomena, tumor location or microenvironmental factors that should be further explored.
Source: Scientific Reports
Institutions: Mayo Clinic, Mayo Clinic in Arizona
Language: English
Subfield: Radiology, Nuclear Medicine and Imaging
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $1,990
Awards: Cancer as a Complex Adaptive System, G2337865930, Patient-specific predictive modeling that integrates advanced cancer imaging, MIT/Mayo Physical Sciences Center for Drug Distribution and Efficacy in Brain Tumors, The Physical Microenvironment in Cancer Biology and Therapy, Novel Tools for Evaluation and Prediction of Radiotherapy Response in Individual, Quantifying Multiscale Competitive Landscapes of Clonal Diversity in Glioblastoma
OpenAlex ID: W3217463677
