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Inicio  /  Cancers  /  Vol: 16 Par: 4 (2024)  /  Artículo
ARTÍCULO
TITULO

PathoGraph: An Attention-Based Graph Neural Network Capable of Prognostication Based on CD276 Labelling of Malignant Glioma Cells

Islam Alzoubi    
Lin Zhang    
Yuqi Zheng    
Christina Loh    
Xiuying Wang and Manuel B. Graeber    

Resumen

We have developed a graph- and attention-based A.I. model (PathoGraph) that predicts survival and identifies tissue areas of special diagnostic interest in whole slide images (WSI) of glioblastoma. CD276, an important immune checkpoint molecule, was confirmed to be a marker of malignant glioma cells/putative glioma stem cells (GSCs). The previously developed PathoFusion framework was used to selectively detect these cells. The graphs and attention scores that were obtained on the basis of this selection provide information that is hidden from conventional microscopic observation.

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