Image-based consensus molecular subtype (imCMS) classification of colorectal cancer using deep learning

Korsuk Sirinukunwattana, Enric Domingo* (Corresponding Author), Susan Richman, Keara L Redmond, Andrew Blake, Clare Verrill, Simon J. Leedham, Aikaterini Chatzipli, Claire Hardy, Celina Whalley, Chieh-Hsi Wu, Andrew D. Beggs, Ultan McDermott, Philip D. Dunne, Angela A Meade, Steven M Walker, Graeme Murray, Leslie M. Samuel, Matthew Seymour, Ian TomlinsonPhilip Quirke, Tim Maughan, Jens Rittscher*, Viktor H. Koelzer*, The S:CORT Consortium

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

62 Citations (Scopus)


OBJECTIVE: Complex phenotypes captured on histological slides represent the biological processes at play in individual cancers, but the link to underlying molecular classification has not been clarified or systematised. In colorectal cancer (CRC), histological grading is a poor predictor of disease progression, and consensus molecular subtypes (CMSs) cannot be distinguished without gene expression profiling. We hypothesise that image analysis is a cost-effective tool to associate complex features of tissue organisation with molecular and outcome data and to resolve unclassifiable or heterogeneous cases. In this study, we present an image-based approach to predict CRC CMS from standard H&E sections using deep learning.

DESIGN: Training and evaluation of a neural network were performed using a total of n=1206 tissue sections with comprehensive multi-omic data from three independent datasets (training on FOCUS trial, n=278 patients; test on rectal cancer biopsies, GRAMPIAN cohort, n=144 patients; and The Cancer Genome Atlas (TCGA), n=430 patients). Ground truth CMS calls were ascertained by matching random forest and single sample predictions from CMS classifier.

RESULTS: Image-based CMS (imCMS) accurately classified slides in unseen datasets from TCGA (n=431 slides, AUC)=0.84) and rectal cancer biopsies (n=265 slides, AUC=0.85). imCMS spatially resolved intratumoural heterogeneity and provided secondary calls correlating with bioinformatic prediction from molecular data. imCMS classified samples previously unclassifiable by RNA expression profiling, reproduced the expected correlations with genomic and epigenetic alterations and showed similar prognostic associations as transcriptomic CMS.

CONCLUSION: This study shows that a prediction of RNA expression classifiers can be made from H&E images, opening the door to simple, cheap and reliable biological stratification within routine workflows.

Original languageEnglish
Pages (from-to)544-554
Number of pages11
Early online date20 Jul 2020
Publication statusPublished - 1 Mar 2021


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