Towards computationally efficient prediction of molecular signatures from routine histology images

Maxime W Lafarge,Viktor H Koelzer

doi:10.1016/s2589-7500(21)00232-6

Abstract

Towards computationally efficient prediction of molecular signatures from routine histology images

Highlights

Identification of actionable genomic alterations in diagnostic tissue samples provides key information for personalised cancer treatment
Current diagnostic tests used to predict the status of molecular pathways from standard histopathology material are commonly tissue destructive, generate relevant costs, and can take hours or even days to return conclusive results
Relating molecular alterations to digital image information derived from standard haematoxylin and eosin-stained histopathology slides has become a task that machine learning models are able to solve across multiple cancer types.[1,2,3,4,5,6,7]

Summary

Introduction

Identification of actionable genomic alterations in diagnostic tissue samples provides key information for personalised cancer treatment. Current diagnostic tests used to predict the status of molecular pathways from standard histopathology material are commonly tissue destructive, generate relevant costs, and can take hours or even days to return conclusive results.

Results

Conclusion