The human-interpretable features (HIF) generated by the convolutional neural networks (CNN) models showed that the composition of the tumor microenvironment (TME) is significantly different in tissue, including lymphocyte density, when c-MET is overexpressed.
Multivariate regression (MR) based on TME features and graph neural networks (GNN) models was able to identify patients with tumors that overexpressed c-MET; however, the performance of the GNN model was stronger.
These results indicate there is a potential to develop and validate an H&E-based screening tool for patient selection for c-MET targeting therapies, thereby increasing efficiency and access. Rajan et al.
Multivariate regression (MR) based on TME features and graph neural networks (GNN) models was able to identify patients with tumors that overexpressed c-MET; however, the performance of the GNN model was stronger.
These results indicate there is a potential to develop and validate an H&E-based screening tool for patient selection for c-MET targeting therapies, thereby increasing efficiency and access. Rajan et al.
