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Kiechle, J. ; Fischer, S.M. ; Lang, D.M. ; di Folco, M. ; Foreman, S.C.* ; Rösner, V.K.N.* ; Lohse, A.K.* ; Mogler, C.* ; Knebel, C.* ; Makowski, M.R.* ; Woertler, K.* ; Combs, S.E.* ; Gersing, A.S.* ; Peeken, J.C. ; Schnabel, J.A.

Unifying local and global shape descriptors to grade soft-tissue sarcomas using graph convolutional networks.

In: (Proceedings - International Symposium on Biomedical Imaging). 345 E 47th St, New York, Ny 10017 Usa: Ieee, 2024. DOI: 10.1109/ISBI56570.2024.10635799 (Proceedings - International Symposium on Biomedical Imaging)

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The tumor grading of patients suffering from soft-tissue sarcomas is a critical task, as an accurate classification of this high-mortality cancer entity constitutes a decisive factor in devising optimal treatment strategies. In this work, we focus on distinguishing soft-tissue sarcoma subtypes solely based on their 3D morphological characteristics, derived from tumor segmentation masks. Notably, we direct attention to overcoming the limitations of texture-based methodologies, which often fall short of providing adequate shape delineation. To this end, we propose a novel yet elegant modular geometric deep learning framework coined Global Local Graph Convolutional Network (GloLo-GCN) that integrates local and global shape characteristics into a meaningful unified shape descriptor. Evaluated on a multi-center dataset, our proposed model performs better in soft-tissue sarcoma grading than GCNs based on state-of-the-art graph convolutions and a volumetric 3D convolutional neural network, also evaluated on binary segmentation masks exclusively.

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