Jacqueline Fontugne

This paper develops a deep learning model to predict molecular subtypes and outcomes in muscle-invasive bladder cancer using histopathological images instead of RNA sequencing.

sCellST predicts single-cell gene expression from H&E images using spatial transcriptomics data, outperforming traditional methods in pancreatic cancer and detecting morphological differences in ovarian cancer.

This study identifies molecular subgroups of BPH and suggests mTOR inhibitors as a potential therapy for specific subtypes.