Development and validation of deep learning classifiers to detect Epstein-Barr virus and microsatellite instability status in gastric cancer: a retrospective multicentre cohort study
The Lancet Digital Health, vol. 3, pp. e654–e664
Abstract
BACKGROUND: Response to immunotherapy in gastric cancer is associated with microsatellite instability (or mismatch repair deficiency) and Epstein-Barr virus (EBV) positivity. We therefore aimed to develop and validate deep learning-based classifiers to detect microsatellite instability and EBV status from routine histology slides. METHODS: In this retrospective, multicentre study, we collected tissue samples from ten cohorts of patients with gastric cancer from seven countries (South Korea, Switzerland, Japan, Italy, Germany, the UK and the USA). We trained a deep learning-based classifier to detect microsatellite instability and EBV positivity from digitised, haematoxylin and eosin stained resection slides without annotating tumour containing regions. The performance of the classifier was assessed by within-cohort cross-validation in all ten cohorts and by external validation, for which we split the cohorts into a five-cohort training dataset and a five-cohort test dataset. We measured the area under the receiver operating curve (AUROC) for detection of microsatellite instability and EBV status. Microsatellite instability and EBV status were determined to be detectable if the lower bound of the 95% CI for the AUROC was above 0·5. FINDINGS: Across the ten cohorts, our analysis included 2823 patients with known microsatellite instability status and 2685 patients with known EBV status. In the within-cohort cross-validation, the deep learning-based classifier could detect microsatellite instability status in nine of ten cohorts, with AUROCs ranging from 0·597 (95% CI 0·522-0·737) to 0·836 (0·795-0·880) and EBV status in five of eight cohorts, with AUROCs ranging from 0·819 (0·752-0·841) to 0·897 (0·513-0·966). Training a classifier on the pooled training dataset and testing it on the five remaining cohorts resulted in high classification performance with AUROCs ranging from 0·723 (95% CI 0·676-0·794) to 0·863 (0·747-0·969) for detection of microsatellite instability and from 0·672 (0·403-0·989) to 0·859 (0·823-0·919) for detection of EBV status. INTERPRETATION: Classifiers became increasingly robust when trained on pooled cohorts. After prospective validation, this deep learning-based tissue classification system could be used as an inexpensive predictive biomarker for immunotherapy in gastric cancer. FUNDING: German Cancer Aid and German Federal Ministry of Health.
Authors 35
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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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Universitätsklinikum Aachen · RWTH Aachen University
Affiliation as printed
Department of Surgery and Transplantation, University Hospital RWTH Aachen, Aachen, Germany; Institute of Pathology, University Hospital RWTH Aachen, Aachen, Germany
Department of Surgery and Transplantation, University Hospital RWTH Aachen, Aachen, Germany
Institute of Pathology, University Hospital RWTH Aachen, Aachen, Germany
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Technical University of Munich
Affiliation as printed
Institute of Pathology, TUM School of Medicine, Technical University of Munich, Munich, Germany
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Technical University of Munich
Affiliation as printed
Institute of Pathology, TUM School of Medicine, Technical University of Munich, Munich, Germany
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University of Bern · Johannes Kepler University of Linz · University Hospital of Bern · Kepler Universitätsklinikum
Affiliation as printed
Institute of Pathology, Inselspital, University of Bern, Switzerland; Institute of Pathology and Molecular Pathology, Kepler University Hospital, Johannes Kepler University Linz, Linz, Austria
Institute of Pathology and Molecular Pathology, Kepler University Hospital, Johannes Kepler University Linz, Linz, Austria
Institute of Pathology, Inselspital, University of Bern, Switzerland
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University of Bern · University Hospital of Bern
Affiliation as printed
Institute of Pathology, Inselspital, University of Bern, Switzerland
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Yonsei University · Yonsei University Health System
Affiliation as printed
Department of Surgery, Yonsei University Health System, Yonsei University College of Medicine, Seoul, South Korea
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Affiliation as printed
Center for Gastric Cancer, National Cancer Center, Goyang, South Korea
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Yonsei University · Yonsei University Health System
Affiliation as printed
Department of Pathology, Yonsei University College of Medicine, Seoul, South Korea
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National Cancer Center · National Cancer Center Hospital
Affiliation as printed
Department of Pathology, National Cancer Center, Goyang, South Korea
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Royal Marsden NHS Foundation Trust · Royal Marsden Hospital
Affiliation as printed
Department of Medicine, Gastrointestinal and Lymphoma Units, The Royal Marsden NHS Foundation Trust, London, UK
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Affiliation as printed
Department of Surgery, Royal Marsden Hospital, London, UK
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MRC Clinical Trials Unit at UCL · University College London · Medical Research Council
Affiliation as printed
Medical Research Council Clinical Trials Unit, University College London, London, UK
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MRC Clinical Trials Unit at UCL · University College London · Medical Research Council
Affiliation as printed
Medical Research Council Clinical Trials Unit, University College London, London, UK
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Affiliation as printed
Pathology and Data Analytics, Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, UK
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St James's University Hospital
Affiliation as printed
Department of Oesophago-Gastric Surgery, St James's University Hospital, Leeds, UK
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Affiliation as printed
Pathology and Data Analytics, Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, UK
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Affiliation as printed
Pathology and Data Analytics, Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, UK
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Affiliation as printed
Department of Gastric Surgery, National Cancer Center Hospital, Tokyo, Japan
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Affiliation as printed
Department of Gastrointestinal Surgery, Kanagawa Cancer Center, Yokohama, Japan
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Affiliation as printed
Institute of Pathology and Molecular Diagnostics, University Hospital Augsburg, Augsburg, Germany
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Affiliation as printed
Institute of Pathology and Molecular Diagnostics, University Hospital Augsburg, Augsburg, Germany
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Affiliation as printed
Department of Medicine, Surgery and Neuroscience, Unit of General Surgery and Surgical Oncology, University of Siena, Italy
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Affiliation as printed
Department of Medicine, Surgery and Neuroscience, Unit of General Surgery and Surgical Oncology, University of Siena, Italy
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Affiliation as printed
Institute of Pathology, University Hospital Cologne, Cologne, Germany
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Affiliation as printed
Department of General, Visceral, Cancer and Transplantation Surgery, University Hospital Cologne, Cologne, Germany
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Universitätsklinikum Aachen · RWTH Aachen University
Affiliation as printed
Department of Surgery and Transplantation, University Hospital RWTH Aachen, Aachen, Germany
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University of Chicago · University of Chicago Medical Center
Affiliation as printed
Department of Medicine, University of Chicago Medicine, Chicago, IL, USA
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Düsseldorf University Hospital
Affiliation as printed
Department of Gastroenterology, Hepatology and Infectious Diseases, University Hospital Duesseldorf, Düsseldorf, Germany
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Heidelberg University · Medizinische Fakultät Mannheim
Affiliation as printed
Department of Medicine II, Mannheim Institute for Innate Immunoscience and Clinical Cooperation Unit Healthy Metabolism, Center of Preventive Medicine and Digital Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
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University Hospital Heidelberg · National Center for Tumor Diseases
Affiliation as printed
Department of Medical Oncology, National Center for Tumor Diseases, University Hospital Heidelberg, 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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Universitätsklinikum Aachen · RWTH Aachen University
Affiliation as printed
Institute of Pathology, University Hospital RWTH Aachen, Aachen, Germany
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University of Leeds · Maastricht University Medical Centre · Maastricht University
Affiliation as printed
Pathology and Data Analytics, Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, UK; Department of Pathology, GROW School for Oncology and Developmental Biology, Maastricht University Medical Center+, Maastricht, Netherlands
Department of Pathology, GROW School for Oncology and Developmental Biology, Maastricht University Medical Center+, Maastricht, Netherlands
Pathology and Data Analytics, Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, UK
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University of Leeds · Heidelberg University · University Hospital Heidelberg · National Center for Tumor Diseases · Universitätsklinikum Aachen · RWTH Aachen University
Affiliation as printed
Department of Medicine III, University Hospital RWTH Aachen, Aachen, Germany; Pathology and Data Analytics, Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, UK; Department of Medical Oncology, National Center for Tumor Diseases, University Hospital Heidelberg, Heidelberg, Germany. Electronic address: jkather@ukaachen.de
Department of Medical Oncology, National Center for Tumor Diseases, University Hospital Heidelberg, Heidelberg, Germany
Department of Medicine III, University Hospital RWTH Aachen, Aachen, Germany
Pathology and Data Analytics, Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, UK
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