A deep learning model to predict RNA-Seq expression of tumours from whole slide images - Archive ouverte HAL Access content directly
Journal Articles Nature Communications Year : 2020

A deep learning model to predict RNA-Seq expression of tumours from whole slide images

(1) , (1) , (1) , (1) , (2, 3) , (2, 3) , (1) , (1) , (1) , (1) , (1) , (1) , (1) , (1)
1
2
3

Abstract

Deep learning methods for digital pathology analysis are an effective way to address multiple clinical questions, from diagnosis to prediction of treatment outcomes. These methods have also 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 show that HE2RNA, a model based on the integration of multiple data modes, can be trained to systematically predict RNA-Seq profiles from whole-slide images alone, without expert annotation. Through its interpretable design, HE2RNA provides virtual spatialization of gene expression, as validated by CD3- and CD20-staining on an independent dataset. The transcriptomic representation learned by HE2RNA can also be transferred on other datasets, even of small size, to increase prediction performance for specific molecular phenotypes. We illustrate the use of this approach in clinical diagnosis purposes such as the identification of tumors with microsatellite instability.
Fichier principal
Vignette du fichier
s41467-020-17678-4.pdf (5.72 Mo) Télécharger le fichier
Origin : Publication funded by an institution
Loading...

Dates and versions

inserm-02951135 , version 1 (28-09-2020)

Identifiers

Cite

Benoît Schmauch, Alberto Romagnoni, Elodie Pronier, Charlie Saillard, Pascale Maillé, et al.. A deep learning model to predict RNA-Seq expression of tumours from whole slide images. Nature Communications, 2020, 11 (1), pp.3877. ⟨10.1038/s41467-020-17678-4⟩. ⟨inserm-02951135⟩

Collections

INSERM IMRB UPEC
140 View
78 Download

Altmetric

Share

Gmail Facebook Twitter LinkedIn More