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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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: 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
Brenner, Hermann
Chang-Claude, Jenny
Hoffmeister, Michael
Halama, Niels
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: gold
doi.org
Licence: cc-by
creativecommons.org/licenses/by/4.0/ (vor)
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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Predicting survival from colorectal cancer histology slides using deep learning: A retrospective multicenter study

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