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

Verrill, Clare; Sirinukunwattana, Korsuk; Domingo, Enric; Blake, A.; Leedham, Simon J.; Ch, Wu; Maughan, Tim; Rittscher, Jens; Koelzer, Viktor H.

doi:10.1101/645143

Cited by 32 publications

(33 citation statements)

References 34 publications

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“…It is therefore not too difficult to foresee how this may be utilised in a computationally augmented histopathology workflow enabling more precise and faster diagnosis and prognosis. Further, the ability to quantify a rich set of histopathology patterns lays out a path to define integrated histopathology and molecular cancer subtypes, as recently demonstrated for colorectal cancers 47 . Lastly, our analyses provide proof-of-concept for these principles and we expect them to be greatly refined in the future based on larger training corpora and further algorithmic refinements.…”

Section: Discussionmentioning

confidence: 94%

Pan-cancer computational histopathology reveals mutations, tumor composition and prognosis

Jung

Torné

et al. 2019

Preprint

109

166

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Key points• Pan-cancer computational histopathology analysis with deep learning extracts histopathological patterns and accurately discriminates 28 cancer and 14 normal tissue types • Computational histopathology predicts whole genome duplications, focal amplifications and deletions, as well as driver gene mutations • Wide-spread correlations with gene expression indicative of immune infiltration and proliferation • Prognostic information augments conventional grading and histopathology subtyping in the majority of cancers AbstractHere we use deep transfer learning to quantify histopathological patterns across 17,396 H&E stained histopathology image slides from 28 cancer types and correlate these with underlying genomic and transcriptomic data. Pan-cancer computational histopathology (PC-CHiP) classifies the tissue origin across organ sites and provides highly accurate, spatially resolved tumor and normal distinction within a given slide. The learned computational histopathological features correlate with a large range of recurrent genetic aberrations, including whole genome duplications (WGDs), arm-level copy number gains and losses, focal amplifications and deletions as well as driver gene mutations within a range of cancer types. WGDs can be predicted in 25/27 cancer types (mean AUC=0.79) including those that were not part of model training. Similarly, we observe associations with 25% of mRNA transcript levels, which enables to learn and localise histopathological patterns of molecularly defined cell types on each slide. Lastly, we find that computational histopathology provides prognostic information augmenting histopathological subtyping and grading in the majority of cancers assessed, which pinpoints prognostically relevant areas such as necrosis or infiltrating lymphocytes on each tumour section. Taken together, these findings highlight the large potential of PC-CHiP to discover new molecular and prognostic associations, which can augment diagnostic workflows and lay out a rationale for integrating molecular and histopathological data.

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Section: Discussionmentioning

confidence: 94%

Pan-cancer computational histopathology reveals mutations, tumor composition and prognosis

Jung

Torné

et al. 2019

Preprint

109

166

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“…Although deep learning-based tissue analysis is still in its early phase, few promising results have been published. For example, a recent study reported that deep learning-based molecular cancer subtyping can be performed directly from the standard H&E sections obtained from patients with colorectal cancers (CRCs)[ 13 ]. Microsatellite instability can also be predicted from the tissue slides[ 14 ].…”

Section: Introductionmentioning

confidence: 99%

Prediction of clinically actionable genetic alterations from colorectal cancer histopathology images using deep learning

Jang

Lee

Kang

et al. 2020

WJG

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BACKGROUND Identifying genetic mutations in cancer patients have been increasingly important because distinctive mutational patterns can be very informative to determine the optimal therapeutic strategy. Recent studies have shown that deep learning-based molecular cancer subtyping can be performed directly from the standard hematoxylin and eosin (H&E) sections in diverse tumors including colorectal cancers (CRCs). Since H&E-stained tissue slides are ubiquitously available, mutation prediction with the pathology images from cancers can be a time- and cost-effective complementary method for personalized treatment. AIM To predict the frequently occurring actionable mutations from the H&E-stained CRC whole-slide images (WSIs) with deep learning-based classifiers. METHODS A total of 629 CRC patients from The Cancer Genome Atlas (TCGA-COAD and TCGA-READ) and 142 CRC patients from Seoul St. Mary Hospital (SMH) were included. Based on the mutation frequency in TCGA and SMH datasets, we chose APC , KRAS , PIK3CA , SMAD4 , and TP53 genes for the study. The classifiers were trained with 360 × 360 pixel patches of tissue images. The receiver operating characteristic (ROC) curves and area under the curves (AUCs) for all the classifiers were presented. RESULTS The AUCs for ROC curves ranged from 0.693 to 0.809 for the TCGA frozen WSIs and from 0.645 to 0.783 for the TCGA formalin-fixed paraffin-embedded WSIs. The prediction performance can be enhanced with the expansion of datasets. When the classifiers were trained with both TCGA and SMH data, the prediction performance was improved. CONCLUSION APC , KRAS , PIK3CA , SMAD4 , and TP53 mutations can be predicted from H&E pathology images using deep learning-based classifiers, demonstrating the potential for deep learning-based mutation prediction in the CRC tissue slides.

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“…Working on molecular subtyping, the group of Koelzer using deep learning methods for the identification of Consensus Molecular Subtypes (CMS) on H&E WSI. Their work establishes the H&E image as a surrogate for the CMS group 34 .…”

Section: Discussionmentioning

confidence: 99%

Classification of colorectal tissue images from high throughput tissue microarrays by ensemble deep learning methods

Nguyen

Blank

Dawson

et al. 2021

Sci Rep

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Tissue microarray (TMA) core images are a treasure trove for artificial intelligence applications. However, a common problem of TMAs is multiple sectioning, which can change the content of the intended tissue core and requires re-labelling. Here, we investigate different ensemble methods for colorectal tissue classification using high-throughput TMAs. Hematoxylin and Eosin (H&E) core images of 0.6 mm or 1.0 mm diameter from three international cohorts were extracted from 54 digital slides (n = 15,150 cores). After TMA core extraction and color enhancement, five different flows of independent and ensemble deep learning were applied. Training and testing data with 2144 and 13,006 cores included three classes: tumor, normal or “other” tissue. Ground-truth data were collected from 30 ngTMA slides (n = 8689 cores). A test augmentation is applied to reduce the uncertain prediction. Predictive accuracy of the best method, namely Soft Voting Ensemble of one VGG and one CapsNet models was 0.982, 0.947 and 0.939 for normal, “other” and tumor, which outperformed to independent or ensemble learning with one base-estimator. Our high-accuracy algorithm for colorectal tissue classification in high-throughput TMAs is amenable to images from different institutions, core sizes and stain intensity. It helps to reduce error in TMA core evaluations with previously given labels.

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

Cited by 32 publications

References 34 publications

Pan-cancer computational histopathology reveals mutations, tumor composition and prognosis

Pan-cancer computational histopathology reveals mutations, tumor composition and prognosis

Prediction of clinically actionable genetic alterations from colorectal cancer histopathology images using deep learning

Classification of colorectal tissue images from high throughput tissue microarrays by ensemble deep learning methods

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