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

Korsuk Sirinukunwattana1,2,3, Enric Domingo4, Susan D Richman5

  • 1Institute of Biomedical Engineering (IBME), Department of Engineering Science, University of Oxford, Oxford, UK.

Gut
|July 22, 2020
PubMed

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Summary

Deep learning on H&E images accurately predicts colorectal cancer molecular subtypes (CMS). This image-based CMS approach offers a cost-effective method for cancer stratification, improving routine diagnostic workflows.

Area of Science:

  • Computational pathology
  • Digital pathology
  • Cancer genomics

Background:

  • Histological features in colorectal cancer (CRC) lack clear links to molecular subtypes.
  • Current methods for CRC molecular classification require gene expression profiling, limiting accessibility.
  • Histological grading is an unreliable predictor of CRC progression.

Purpose of the Study:

  • To develop and validate an image-based deep learning model for predicting CRC Consensus Molecular Subtypes (CMS) from standard H&E stained slides.
  • To assess the cost-effectiveness and reliability of image analysis for cancer stratification.
  • To explore the potential of image analysis in resolving intratumoural heterogeneity and unclassifiable cases.

Main Methods:

  • A deep learning neural network was trained on 1206 H&E tissue sections with multi-omic data from three independent cohorts (FOCUS, GRAMPIAN, TCGA).
Keywords:
colorectal pathologycomputerised image analysismolecular pathology

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  • Ground truth CMS calls were established using random forest and single-sample prediction classifiers.
  • The model's performance was evaluated on unseen datasets, including TCGA and rectal cancer biopsies.

    • Main Results:

    • The image-based CMS (imCMS) model achieved high classification accuracy on independent datasets (AUC=0.84 for TCGA, AUC=0.85 for rectal cancer).
    • imCMS demonstrated the ability to spatially resolve intratumoural heterogeneity and provided secondary classifications correlating with molecular data.
    • The model successfully classified previously unclassifiable samples and showed prognostic associations similar to transcriptomic CMS.

    Conclusions:

    • Histopathological images analyzed with deep learning can predict RNA expression-based CRC molecular subtypes.
    • This image-based approach provides a simple, cost-effective, and reliable method for biological stratification in routine pathology workflows.
    • imCMS has the potential to enhance cancer subtyping and improve patient stratification for treatment.