Transcriptomic learning for digital pathology [article]

Benoît Schmauch, Alberto Romagnoni, Elodie Pronier, Charlie Saillard, Pascale Maillé, Julien Calderaro, Meriem Sefta, Sylvain Toldo, Thomas Clozel, Matahi Moarii, Pierre Courtiol, Gilles Wainrib
2019 bioRxiv   pre-print
Deep learning methods for digital pathology analysis have proved an effective way to address multiple clinical questions, from diagnosis to prognosis and the prediction of treatment outcomes. They have also recently been used to predict gene mutations from pathology images, but no comprehensive evaluation of their potential for extracting molecular features from histology slides, has yet been performed. We propose a novel approach based on the integration of multiple data modes, and show that
more » ... r deep learning model HE2RNA can be trained to predict systematically RNA-Seq profiles from whole-slide images alone, without the need for expert annotation. The model facilitates the virtual spatialization of gene expression, as validated by double-staining in an independent dataset. The results can therefore be interpreted in detail and this model opens up new opportunities for virtual staining. Finally, the transcriptomic representation learned by the model could be could be used to improve performances for other clinical tasks, particularly for small datasets. For example we studied the problem of predicting microsatellite instability from Hematoxylin & Eosin (H&E)-stained images. Greater prediction ability was achieved in such a realistic framework.
doi:10.1101/760173 fatcat:mb55w3o4djgvhdbaj2r7mltgey