Comparative Analysis of Radiomic Features and Gene Expression Profiles
  in Histopathology Data Using Graph Neural Networks
Luis Carlos Rivera Monroy Leonhard Rist Martin Eberhardt Christian Ostalecki Andreas Bauer Julio Vera Katharina Breininger Andreas Maier
This study leverages graph neural networks to integrate MELC data with
Radiomic-extracted features for melanoma classification, focusing on cell-wise
analysis. It assesses the effectiveness of gene expression profiles and
Radiomic features, revealing that Radiomic features, particularly when combined
with UMAP for dimensionality reduction, significantly enhance classification
performance. Notably, using Radiomics contributes to increased diagnostic
accuracy and computational efficiency, as it allows for the extraction of
critical data from fewer stains, thereby reducing operational costs. This
methodology marks an advancement in computational dermatology for melanoma cell
classification, setting the stage for future research and potential
developments.