Proceedings Article | 3 April 2024
KEYWORDS: Tumors, Magnetic resonance imaging, Machine learning, Feature extraction, Body composition, Cross validation, Reflection, Random forests, Image segmentation, Education and training
Glioblastoma (GB) tumors are highly aggressive brain tumors with a dismal prognosis of 12-15 months. A significant challenge in the treatment of GB tumors is the differentiation of true tumor progression (TP) from pseudoprogression (PsP), a radiation-induced treatment effect that mimics the appearance of tumor recurrence on clinical MRI scans. While a few approaches have focused on capturing textural differences in image intensities (i.e. radiomics) on MRI to distinguish TP from PsP, in this work, we present a novel approach that seeks to capture Spatial Interactions in image intensities via Graph-based Learning (SIGL) from within and around the lesion to distinguish PsP from TP. Given that the tumor micro-environments across PsP (a benign lesion) and TP (an aggressive phenotype) are fundamentally different, we hypothesize that the spatial interactions in intensity composition within the lesion and its surrounding area, as captured through connected graph features, will be different and thus can distinguish between TP and PsP. Our retrospective analysis was conducted on a total of n = 105 GB patients curated from the Cleveland Clinic (n = 59) and Dana-Farber Cancer Center (n=46). For the purposes of model development and validation, out of the 105 patients, 72 (TP; n = 48, PsP; n = 24) were utilized for training, while the remaining 33 (TP; n = 21, PsP; n = 12) were allocated for testing. Pre-processing, including tumor segmentation and intensity normalization, was conducted, followed by the construction of a weighted tumor graph. A total of 10 graph-based metrics were initially extracted, and a feature selection method was employed to refine this dataset. The selected features were then fed into a Random Forest (RF) classifier to distinguish between True Progression (TP) and Pseudo Progression (PsP). Statistical analyses showed significant differences in 8 of the 10 SIGL features, including size, diameter, radius, average shortest path length, density, small-worldness, number of connected components, and clustering coefficient, between TP and PsP patients. The RF classifier demonstrated an 80% 10-fold cross-validation accuracy and 78% accuracy on the test set using the SIGL features. Preliminary results suggest that the graph-based features may be able to capture the inherent differences in lesion composition between TP and PsP, in GB tumors.
