Deep learning can predict lymph node status directly from histology in colorectal cancer
European Journal of Cancer, vol. 157, pp. 464–473
Abstract
BACKGROUND: Lymph node status is a prognostic marker and strongly influences therapeutic decisions in colorectal cancer (CRC). OBJECTIVES: The objective of the study is to investigate whether image features extracted by a deep learning model from routine histological slides and/or clinical data can be used to predict CRC lymph node metastasis (LNM). METHODS: Using histological whole slide images (WSIs) of primary tumours of 2431 patients in the DACHS cohort, we trained a convolutional neural network to predict LNM. In parallel, we used clinical data derived from the same cases in logistic regression analyses. Subsequently, the slide-based artificial intelligence predictor (SBAIP) score was included in the regression. WSIs and data from 582 patients of the TCGA cohort were used as the external test set. RESULTS: On the internal test set, the SBAIP achieved an area under receiver operating characteristic (AUROC) of 71.0%, the clinical classifier achieved an AUROC of 67.0% and a combination of the two classifiers yielded an improvement to 74.1%. Whereas the clinical classifier's performance remained stable on the TCGA set, performance of the SBAIP dropped to an AUROC of 61.2%. Performance of the clinical classifier depended strongly on the T stage. CONCLUSION: Deep learning-based image analysis may help predict LNM of patients with CRC using routine histological slides. Combination with clinical data such as T stage might be useful. Strategies to increase performance of the SBAIP on external images should be investigated.
Authors 16
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Affiliation as printed
Digital Biomarkers for Oncology Group, German Cancer Research Center (DKFZ), Heidelberg, Germany
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Affiliation as printed
Digital Biomarkers for Oncology Group, German Cancer Research Center (DKFZ), Heidelberg, Germany
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Affiliation as printed
Digital Biomarkers for Oncology Group, German Cancer Research Center (DKFZ), Heidelberg, Germany
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Affiliation as printed
Digital Biomarkers for Oncology Group, German Cancer Research Center (DKFZ), Heidelberg, Germany
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Affiliation as printed
Digital Biomarkers for Oncology Group, German Cancer Research Center (DKFZ), Heidelberg, Germany
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Universitätsklinikum Aachen · RWTH Aachen University
Affiliation as printed
Department of Medicine III, University Hospital RWTH Aachen, Aachen, Germany
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Affiliation as printed
Division of Biostatistics, German Cancer Research Center (DKFZ), Heidelberg, Germany
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Affiliation as printed
Division of Biostatistics, German Cancer Research Center (DKFZ), Heidelberg, Germany
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German Cancer Research Center · University Medical Center Hamburg-Eppendorf · University Cancer Center Hamburg
Affiliation as printed
Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany; Cancer Epidemiology Group, University Cancer Center Hamburg (UCCH), University Medical Center Hamburg-Eppendorf (UKE), Hamburg, Germany
Cancer Epidemiology Group, University Cancer Center Hamburg (UCCH), University Medical Center Hamburg-Eppendorf (UKE), Hamburg, Germany
Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
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Heidelberg University · National Center for Tumor Diseases
Affiliation as printed
Institute of Pathology, University of Heidelberg, Heidelberg, Germany; Tissue Bank of the National Center for Tumor Diseases (NCT), Heidelberg, Germany
Institute of Pathology, University of Heidelberg, Heidelberg, Germany
Tissue Bank of the National Center for Tumor Diseases (NCT), Heidelberg, Germany
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Berlin Institute of Health at Charité - Universitätsmedizin Berlin · Charité - Universitätsmedizin Berlin
Affiliation as printed
Berlin Institute of Health (BIH) and Charité University Medicine, Berlin, Germany
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National Center for Tumor Diseases
Affiliation as printed
Department of Translational Medical Oncology, National Center for Tumor Diseases (NCT), Heidelberg, Germany
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Affiliation as printed
Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
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German Cancer Research Center · National Center for Tumor Diseases · Deutsches Konsortium für Translationale Krebsforschung
Affiliation as printed
Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany; Division of Preventive Oncology, German Cancer Research Center (DKFZ) and National Center for Tumor Diseases (NCT), Heidelberg, Germany; German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany
Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
Division of Preventive Oncology, German Cancer Research Center (DKFZ) and National Center for Tumor Diseases (NCT), Heidelberg, Germany
German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany
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Affiliation as printed
Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
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Titus Josef Brinker corresponding
German Cancer Research Center · Deutsches Konsortium für Translationale Krebsforschung
Affiliation as printed
Digital Biomarkers for Oncology Group, German Cancer Research Center (DKFZ), Heidelberg, Germany; German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany. Electronic address: titus.brinker@dkfz.de
Digital Biomarkers for Oncology Group, German Cancer Research Center (DKFZ), Heidelberg, Germany
German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany
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