Abstract:
Glioma, a prevalent and devastating brain tumor, presents formidable challenges in
diagnosis and prognostication. This study endeavors to enhance glioma grading accuracy
by leveraging 3D MRI data and a comprehensive array of medical features extracted
through the PyRadiomic framework. Given the diverse manifestations of glioma tumors
and their profound impact, an advanced approach is imperative for precise grading. This
investigation meticulously extracts six distinct medical features, including First Order
Statistics, Shape-based (3D), Gray Level Co-occurrence Matrix, Gray Level Run Length
Matrix, Gray Level Size Zone Matrix, Neighboring Gray Tone Difference Matrix, and
Gray Level Dependence Matrix. These features, computed based on tumor annotation,
provide detailed characterizations of glioma tumors, elucidating their intricacies.
To augment glioma grading accuracy further, various machine learning algorithms are
employed. A pivotal contribution is the introduction of the Radiomic Graph Neural
Network (RGNN) model, tailored for graph-based data, where nodes symbolize entities,
and edges denote intricate relationships between them. The core objective of the RGNN
model is to generate low-dimensional vector representations (embeddings) for nodes
within the graph, preserving underlying structural and relational information. This
innovative RGNN model significantly enhances precision in differentiating between
various glioma grades. Specifically, for the Native T1 stage of MRI and T2-weighted
(T2) stages, the proposed RGNN model achieves an unprecedented accuracy of 99.00%.
This outperforms existing methods and sets a new benchmark in glioma tumor grading
based on medical features, leveraging 4 stages of 3D magnetic resonance imaging.
