Jovan Tanevski

The authors present SpaCEy (Spatial Clinical Explainability), an explainable graph neural network that builds each tissue sample into a spatial graph via Delaunay triangulation, with nodes as single-cell protein-marker abundances and no predefined cell-type or anatomical-region inputs, learns predictive embeddings with a GNN, and uses a GNNExplainer-style explainer to output edge masks that are aggregated over k-hop neighbourhoods into node importance, thereby localising contiguous outcome-associated spatial regions and key proteins; it predicted progression in a 416-patient lung adenocarcinoma cohort (accuracy 0.68, F1 0.68, AUC 0.62, versus Ali et al. 0.61/0.59/0.57 and SPACE-GM 0.55/0.52/0.

The authors present SpaCEy (Spatial Clinical Explainability), an explainable graph neural network that builds each tissue sample into a spatial graph via Delaunay triangulation, with nodes as single-cell protein-marker abundances and no predefined cell-type or anatomical-region inputs, learns predictive embeddings with a GNN, and uses a GNNExplainer-style explainer to output edge masks that are aggregated over k-hop neighbourhoods into node importance, thereby localising contiguous outcome-associated spatial regions and key proteins; it predicted progression in a 416-patient lung adenocarcinoma cohort (accuracy 0.68, F1 0.68, AUC 0.62, versus Ali et al. 0.61/0.59/0.57 and SPACE-GM 0.55/0.52/0.