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Cited by 1,082AI in cancer detectionRadiomics and Machine Learning in Medical ImagingColorectal Cancer Screening and Detection

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Predicting survival from colorectal cancer histology slides using deep learning: A retrospective multicenter study

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Kather, Jakob Nikolas, Krisam, Johannes, Charoentong, Pornpimol, Luedde, Tom, Herpel, Esther, Weis, Cleo-Aron, Gaiser, Timo, Marx, Alexander, Valous, Nektarios A., Ferber, Dyke, Jansen, Lina, Reyes-Aldasoro, Constantino Carlos, Zörnig, Inka, Jäger, Dirk, Brenner, Hermann, Chang-Claude, Jenny, Hoffmeister, Michael, Halama, Niels·2019·PLOS Medicine
DOI 10.1371/journal.pmed.1002730PMC PMC6345440
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Abstract

BACKGROUND: For virtually every patient with colorectal cancer (CRC), hematoxylin-eosin (HE)-stained tissue slides are available. These images contain quantitative information, which is not routinely used to objectively extract prognostic biomarkers. In the present study, we investigated whether deep convolutional neural networks (CNNs) can extract prognosticators directly from these widely available images. METHODS AND FINDINGS: We hand-delineated single-tissue regions in 86 CRC tissue slides, yielding more than 100,000 HE image patches, and used these to train a CNN by transfer learning, reaching a nine-class accuracy of >94% in an independent data set of 7,180 images from 25 CRC patients. With this tool, we performed automated tissue decomposition of representative multitissue HE images from 862 HE slides in 500 stage I-IV CRC patients in the The Cancer Genome Atlas (TCGA) cohort, a large international multicenter collection of CRC tissue. Based on the output neuron activations in the CNN, we calculated a "deep stroma score," which was an independent prognostic factor for overall survival (OS) in a multivariable Cox proportional hazard model (hazard ratio [HR] with 95% confidence interval [CI]: 1.99 [1.27-3.12], p = 0.0028), while in the same cohort, manual quantification of stromal areas and a gene expression signature of cancer-associated fibroblasts (CAFs) were only prognostic in specific tumor stages. We validated these findings in an independent cohort of 409 stage I-IV CRC patients from the "Darmkrebs: Chancen der Verhütung durch Screening" (DACHS) study who were recruited between 2003 and 2007 in multiple institutions in Germany. Again, the score was an independent prognostic factor for OS (HR 1.63 [1.14-2.33], p = 0.008), CRC-specific OS (HR 2.29 [1.5-3.48], p = 0.0004), and relapse-free survival (RFS; HR 1.92 [1.34-2.76], p = 0.0004). A prospective validation is required before this biomarker can be implemented in clinical workflows. CONCLUSIONS: In our retrospective study, we show that a CNN can assess the human tumor microenvironment and predict prognosis directly from histopathological images.

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Figures

Fig 1 Example images for each of the nine tissue classes represented in the NCT-CRC-HE-100K data set. ADI, adipose tissue; BACK, background; CRC, colorectal cancer; DEB, debris; HE, hematoxylin–eosin; LYM, lymphocytes; MUC, mucus; MUS, smooth muscle; NCT, National Center for Tumor Diseases; NORM, normal colon mucosa; STR, cancer-associated stroma; TUM, colorectal adenocarcinoma epithelium.
Fig 1 Example images for each of the nine tissue classes represented in the NCT-CRC-HE-100K data set. ADI, adipose tissue; BACK, background; CRC, colorectal cancer; DEB, debris; HE, hematoxylin–eosin; LYM, lymphocytes; MUC, mucus; MUS, smooth muscle; NCT, National Center for Tumor Diseases; NORM, normal colon mucosa; STR, cancer-associated stroma; TUM, colorectal adenocarcinoma epithelium.CC BY · PubMed Central
Fig 2 A CNN learns robust representations of histological images and attains high classification accuracy. (A) A nine-class training set containing 100,000 unique images and a testing set of 7,180 unique images. Classes are adipose, background, debris, lymphocytes, mucus, smooth muscle, normal mucosa, stroma, cancer epithelium. Pie area is proportional to sample number. (B) Confusion matrix of the CNN-based classification; overall accuracy is 94%. (C) tSNE of the testing set based on deep layer activations of the trained CNN. Tissue classes naturally aggregate in separate clusters, with close proximity of the TUM and NORM cluster and the MUS and STR cluster, respectively. (D) Deep dream visualization of the spatial patterns represented in the trained CNN. For all tissue classes, the network has learned to visually discern key features. For example, LYM are composed of tightly collected small round cells, and NORM is composed of glands in an even distribution pattern. ADI, adipose tissue; BACK, background; CNN, convolutional neural network; DEB, debris; LYM, lymphocytes; MUC, mucus; MUS, smooth muscle; NORM, normal mucosa; STR, stroma; tSNE, t-distributed stochastic neighbor embedding; TUM, cancer epithelium.
Fig 2 A CNN learns robust representations of histological images and attains high classification accuracy. (A) A nine-class training set containing 100,000 unique images and a testing set of 7,180 unique images. Classes are adipose, background, debris, lymphocytes, mucus, smooth muscle, normal mucosa, stroma, cancer epithelium. Pie area is proportional to sample number. (B) Confusion matrix of the CNN-based classification; overall accuracy is 94%. (C) tSNE of the testing set based on deep layer activations of the trained CNN. Tissue classes naturally aggregate in separate clusters, with close proximity of the TUM and NORM cluster and the MUS and STR cluster, respectively. (D) Deep dream visualization of the spatial patterns represented in the trained CNN. For all tissue classes, the network has learned to visually discern key features. For example, LYM are composed of tightly collected small round cells, and NORM is composed of glands in an even distribution pattern. ADI, adipose tissue; BACK, background; CNN, convolutional neural network; DEB, debris; LYM, lymphocytes; MUC, mucus; MUS, smooth muscle; NORM, normal mucosa; STR, stroma; tSNE, t-distributed stochastic neighbor embedding; TUM, cancer epithelium.CC BY · PubMed Central
Fig 3 A CNN can segment histopathological whole-slide images. The neural network classifier was used to classify real-world images from the DACHS cohort. (A) and (B) show two representative example images. Left: original HE image; right: classification map. Even fine structures are recognized by the neural network even in regions of suboptimal tissue quality. Only the tissue is shown in this example, and because the tissue does not occupy a rectangular area on the pathology slide, the whole-slide image was manually segmented by an observer trained in pathology to show only tissue without background for better clarity (background is white). ADI, adipose tissue; BACK, background; CNN, convolutional neural network; DACHS, Darmkrebs: Chancen der Verhütung durch Screening; DEB, debris; HE, hematoxylin–eosin; LYM, lymphocyte aggregates; MUC, mucus; MUS, muscle; NORM, normal mucosa; STR, stroma; TUM, tumor epithelium.
Fig 3 A CNN can segment histopathological whole-slide images. The neural network classifier was used to classify real-world images from the DACHS cohort. (A) and (B) show two representative example images. Left: original HE image; right: classification map. Even fine structures are recognized by the neural network even in regions of suboptimal tissue quality. Only the tissue is shown in this example, and because the tissue does not occupy a rectangular area on the pathology slide, the whole-slide image was manually segmented by an observer trained in pathology to show only tissue without background for better clarity (background is white). ADI, adipose tissue; BACK, background; CNN, convolutional neural network; DACHS, Darmkrebs: Chancen der Verhütung durch Screening; DEB, debris; HE, hematoxylin–eosin; LYM, lymphocyte aggregates; MUC, mucus; MUS, muscle; NORM, normal mucosa; STR, stroma; TUM, tumor epithelium.CC BY · PubMed Central
Fig 4 Prognostication of CRC outcome by a deep stroma score. (A) HE images in the TCGA cohort had heterogeneous texture, and some had poor quality. Image size is 1,500 × 1,500 px, and regions were classified with a sliding window of 224 × 224 px. (B) Neural network activations corresponding to the images shown in panel A are visualized. Even in the poor-quality case, tissue structures are recognized by the network. (C) A deep stroma score based on neural network activations is defined as the weighted sum of stromal tissue classes that are above threshold. (D, E) Mean output layer activation for lymphocytes and stroma separated by CMS. Activation of (D) lymphocytes and (E) stroma were assessed in images from 425 patients from the TCGA cohort. As expected, CMS1 highly activated the lymphocyte output neuron, while CMS4 highly activates the stroma output neuron. *p ≤ 0.05, **p ≤ 0.01; ns > 0.05; two-tailed t test for each group versus all samples. The dashed line marks the mean of all samples against which t test was performed. The line within each box marks the median of that group, the full box contains all samples between the 25th and the 75th percentile, and the vertical lines extend to the smallest and largest nonoutlier value (R ggplot2 geom_boxplot convention). CMS, consensus molecular subtype; CRC, colorectal cancer; HE, hematoxylin–eosin; LYM, lymphocytes; NA, not available; ns, not significant; px, pixels; STR, stroma; TCGA, The Cancer Genome Atlas.
Fig 4 Prognostication of CRC outcome by a deep stroma score. (A) HE images in the TCGA cohort had heterogeneous texture, and some had poor quality. Image size is 1,500 × 1,500 px, and regions were classified with a sliding window of 224 × 224 px. (B) Neural network activations corresponding to the images shown in panel A are visualized. Even in the poor-quality case, tissue structures are recognized by the network. (C) A deep stroma score based on neural network activations is defined as the weighted sum of stromal tissue classes that are above threshold. (D, E) Mean output layer activation for lymphocytes and stroma separated by CMS. Activation of (D) lymphocytes and (E) stroma were assessed in images from 425 patients from the TCGA cohort. As expected, CMS1 highly activated the lymphocyte output neuron, while CMS4 highly activates the stroma output neuron. *p ≤ 0.05, **p ≤ 0.01; ns > 0.05; two-tailed t test for each group versus all samples. The dashed line marks the mean of all samples against which t test was performed. The line within each box marks the median of that group, the full box contains all samples between the 25th and the 75th percentile, and the vertical lines extend to the smallest and largest nonoutlier value (R ggplot2 geom_boxplot convention). CMS, consensus molecular subtype; CRC, colorectal cancer; HE, hematoxylin–eosin; LYM, lymphocytes; NA, not available; ns, not significant; px, pixels; STR, stroma; TCGA, The Cancer Genome Atlas.CC BY · PubMed Central
Fig 5 Deep stroma score is an independent prognosticator for shorter OS in the TCGA cohort. HRs with 95% CI in multivariable Cox models including cancer stage (I–IV), sex, and age for a CAF gene expression score, pathologist’s manual quantification of stromal percentage as provided in the TCGA metadata and the deep stroma score. The deep stroma score was binarized into high/low at the median. The other scores (CAF, pathologist) were binarized at an optimal threshold (optimal Youden index). Only the deep stroma score was significantly associated with prognosis in the whole cohort (stage I–IV). The horizontal axis is scaled logarithmically (log 10). CAF, cancer-associated fibroblast; CI, confidence interval; HR, hazard ratio; OS, overall survival; TCGA, The Cancer Genome Atlas.
Fig 5 Deep stroma score is an independent prognosticator for shorter OS in the TCGA cohort. HRs with 95% CI in multivariable Cox models including cancer stage (I–IV), sex, and age for a CAF gene expression score, pathologist’s manual quantification of stromal percentage as provided in the TCGA metadata and the deep stroma score. The deep stroma score was binarized into high/low at the median. The other scores (CAF, pathologist) were binarized at an optimal threshold (optimal Youden index). Only the deep stroma score was significantly associated with prognosis in the whole cohort (stage I–IV). The horizontal axis is scaled logarithmically (log 10). CAF, cancer-associated fibroblast; CI, confidence interval; HR, hazard ratio; OS, overall survival; TCGA, The Cancer Genome Atlas.CC BY · PubMed Central
Fig 6 Deep stroma score applied to the validation data set (DACHS cohort). HRs with 95% CI in multivariable Cox models including cancer stage (I–IV), sex, and age for the deep stroma score. HR for OS, DSS, and RFS are plotted. The deep stroma score was stratified into high/low at the median of the training set. In this validation cohort, the deep stroma score was an independent prognostic factor over all stages and within stage III and stage IV tumors. The horizontal axis is scaled logarithmically (log 10). CI, confidence interval; DACHS, Darmkrebs: Chancen der Verhütung durch Screening; DSS, disease-specific survival; HR, hazard ratio; OS, overall survival; RFS, relapse-free survival.
Fig 6 Deep stroma score applied to the validation data set (DACHS cohort). HRs with 95% CI in multivariable Cox models including cancer stage (I–IV), sex, and age for the deep stroma score. HR for OS, DSS, and RFS are plotted. The deep stroma score was stratified into high/low at the median of the training set. In this validation cohort, the deep stroma score was an independent prognostic factor over all stages and within stage III and stage IV tumors. The horizontal axis is scaled logarithmically (log 10). CI, confidence interval; DACHS, Darmkrebs: Chancen der Verhütung durch Screening; DSS, disease-specific survival; HR, hazard ratio; OS, overall survival; RFS, relapse-free survival.CC BY · PubMed Central

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Metadata

Authors
Kather, J. N., Krisam, J., Charoentong, P., Luedde, T., Herpel, E., Weis, C.-A., Gaiser, T., Marx, A., Valous, N. A., Ferber, D., Jansen, L., Reyes-Aldasoro, C. C., Zörnig, I., Jäger, D., Brenner, H., Chang-Claude, J., Hoffmeister, M., & Halama, N.
Affiliations
  • German Cancer Research Center (DE)
  • University Hospital Heidelberg (DE)
  • Universitätsklinikum Aachen (DE)
  • Heidelberg University (DE)
  • University Medical Centre Mannheim (DE)
Corresponding author
Kather, Jakob Nikolas
ORCID
Venue
PLOS Medicine
Publisher
Public Library of Science (PLoS)
Published
2019-01-24
Volume
16
Issue
1
Pages
e1002730
ISSN
  • 1549-1277 (ISSN-L)
  • 1549-1676
DOI
10.1371/journal.pmed.1002730
PMC
PMC6345440
OpenAlex
W2914568698
Cited by
1,082
References
52
Citations by year
2026: 158 · 2025: 164 · 2024: 180 · 2023: 170 · 2022: 175 · 2021: 129 · 2020: 87 · 2019: 17
FWCI
46.54
Type
journal article
Language
en
Open access
Licence
Locations
  • PLoS Medicine (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • City Research Online (City University London) (acceptedVersion)
  • RWTH Publications (RWTH Aachen) (submittedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • Figshare (submittedVersion, cc-by)
  • RWTH Publications (RWTH Aachen)
Funders
  • Deutsches Krebsforschungszentrum (Heidelberg School of Oncology)
  • Bundesministerium für Bildung und Forschung (13GW0091E, 01ER0814, 01ER0815, 01ER1505A, 01ER1505B)
  • Deutsche Forschungsgemeinschaft (BR 1704/6-1, BR 1704/6-3, BR 1704/6-4, BR 1704/17-1, CH 117/1-1, and HO 5117/2-1)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
  • Health Sciences › Medicine › Oncology › Colorectal Cancer Screening and Detection
Keywords
colorectal cancer, deep convolutional neural networks, tumor stroma, prognostic factors, overall survival, cancer-associated fibroblasts, cancer of unknown primary, relapse-free survival, TCGA, transfer learning, histopathology image analysis, digital pathology
MeSH terms
Deep Learning, Colon, Coloring Agents, Eosine Yellowish-(YS), Female, Hematoxylin, Humans, Image Interpretation, Computer-Assisted, Male, Prognosis, Rectum, Retrospective Studies, Colorectal Neoplasms

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