Evidence map›Paper›PMID 32562722›Full record

ArticleGastroenterology2020

Clinical-Grade Detection of Microsatellite Instability in Colorectal Tumors by Deep Learning.

Amelie Echle, Heike Irmgard Grabsch, Philip Quirke, Piet A van den Brandt, Nicholas P West, Gordon G A Hutchins, Lara R Heij, Xiuxiang Tan, Susan D Richman, Jeremias Krause and 13 more

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PubMed Full text at PMC Publisher
In one paragraph

Article in Gastroenterology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 168 papers, 6 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
168citing papers in PubMed, 6 pooled it
33.3field-weighted citation impact, top 1% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

168 citing papers in PubMed, 6 syntheses or guidelines pooled it, 361 citations in OpenAlex.

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108 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

23 authors at 8 institutions in 4 countries.

Amelie EchleDepartment of Medicine III, University Hospital RWTH Aachen, Aachen, Germany.
Heike Irmgard GrabschDepartment of Pathology, GROW School for Oncology and Developmental Biology, Maastricht University Medical Center+, Maastricht, The Netherlands; Pathology and Data Analytics, Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, United Kingdom.
Philip QuirkePathology and Data Analytics, Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, United Kingdom.
Piet A van den BrandtDepartment of Epidemiology, Maastricht University Medical Center+, Maastricht, The Netherlands.
Nicholas P WestPathology and Data Analytics, Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, United Kingdom.
Gordon G A HutchinsPathology and Data Analytics, Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, United Kingdom.
Lara R HeijVisceral and Transplant Surgery, University Hospital Rheinisch-Westfälische Technische Hochschule Aachen, Aachen, Germany; NUTRIM School of Nutrition and Translational Research in Metabolism, Maastricht University, Maastricht, the Netherlands; Institute of Pathology, University Hospital RWTH Aachen, Aachen, Germany.
Xiuxiang TanVisceral and Transplant Surgery, University Hospital Rheinisch-Westfälische Technische Hochschule Aachen, Aachen, Germany; NUTRIM School of Nutrition and Translational Research in Metabolism, Maastricht University, Maastricht, the Netherlands; Institute of Pathology, University Hospital RWTH Aachen, Aachen, Germany.
Susan D RichmanPathology and Data Analytics, Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, United Kingdom.
Jeremias KrauseDepartment of Medicine III, University Hospital RWTH Aachen, Aachen, Germany.
Elizabeth AlwersDivision of Clinical Epidemiology and Aging Research, German Cancer Research Center, Heidelberg, Germany.
Josien JenniskensDepartment of Epidemiology, Maastricht University Medical Center+, Maastricht, The Netherlands.
Kelly OffermansDepartment of Epidemiology, Maastricht University Medical Center+, Maastricht, The Netherlands.
Richard GrayClinical Trial Service Unit, University of Oxford, Oxford, United Kingdom.
Hermann BrennerDivision of Clinical Epidemiology and Aging Research, German Cancer Research Center, Heidelberg, Germany; Division of Preventive Oncology, German Cancer Research Center and National Center for Tumor Diseases, Heidelberg, Germany; German Cancer Consortium, German Cancer Research Center, Heidelberg, Germany.
Jenny Chang-ClaudeDivision of Cancer Epidemiology, German Cancer Research Center, Heidelberg, Germany; Cancer Epidemiology Group, University Cancer Center Hamburg, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.
Christian TrautweinDepartment of Medicine III, University Hospital RWTH Aachen, Aachen, Germany.
Alexander T PearsonSection of Hematology/Oncology, Department of Medicine, University of Chicago, Chicago, Illinois.
Peter BoorInstitute of Pathology, University Hospital RWTH Aachen, Aachen, Germany.
Tom LueddeDepartment of Medicine III, University Hospital RWTH Aachen, Aachen, Germany; Division of Gastroenterology, Hepatology, and Hepatobiliary Oncology, Aachen, Germany.
Nadine Therese GaisaInstitute of Pathology, University Hospital RWTH Aachen, Aachen, Germany.
Michael HoffmeisterDivision of Clinical Epidemiology and Aging Research, German Cancer Research Center, Heidelberg, Germany.
Jakob Nikolas KatherDepartment 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, United Kingdom; German Cancer Consortium, German Cancer Research Center, Heidelberg, Germany; Medical Oncology, National Center for Tumor Diseases, University Hospital Heidelberg, Heidelberg, Germany. Electronic address: jkather@ukaachen.de.
University of Leeds · GBMaastricht University · NLRWTH Aachen University · DEGerman Cancer Research Center · DEUniversität Hamburg · DEUniversitätsklinikum Aachen · DEUniversity of Chicago · USUniversity of Oxford · GB

Funding

VIRAL ONCOLOGY CORE FACILITYP30CA014599 · NCI · UNIVERSITY OF CHICAGO · PI MARK W. LINGEN · 1985 to 2026
$122.1M
A reasoned approach to combination therapy development in head and neck cancerK08DE026500 · NIDCR · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI PEARSON, ALEXANDER THOMAS · 2016 to 2020
$800k
Department of HealthNCI NIH HHS P30 CA014599NIDCR NIH HHS K08 DE026500
6 · The paper itself

Abstract

BACKGROUND &

aimsMicrosatellite instability (MSI) and mismatch-repair deficiency (dMMR) in colorectal tumors are used to select treatment for patients. Deep learning can detect MSI and dMMR in tumor samples on routine histology slides faster and less expensively than molecular assays. However, clinical application of this technology requires high performance and multisite validation, which have not yet been performed.

methodsWe collected H&E-stained slides and findings from molecular analyses for MSI and dMMR from 8836 colorectal tumors (of all stages) included in the MSIDETECT consortium study, from Germany, the Netherlands, the United Kingdom, and the United States. Specimens with dMMR were identified by immunohistochemistry analyses of tissue microarrays for loss of MLH1, MSH2, MSH6, and/or PMS2. Specimens with MSI were identified by genetic analyses. We trained a deep-learning detector to identify samples with MSI from these slides; performance was assessed by cross-validation (N = 6406 specimens) and validated in an external cohort (n = 771 specimens). Prespecified endpoints were area under the receiver operating characteristic (AUROC) curve and area under the precision-recall curve (AUPRC).

resultsThe deep-learning detector identified specimens with dMMR or MSI with a mean AUROC curve of 0.92 (lower bound, 0.91; upper bound, 0.93) and an AUPRC of 0.63 (range, 0.59-0.65), or 67% specificity and 95% sensitivity, in the cross-validation development cohort. In the validation cohort, the classifier identified samples with dMMR with an AUROC of 0.95 (range, 0.92-0.96) without image preprocessing and an AUROC of 0.96 (range, 0.93-0.98) after color normalization.

conclusionsWe developed a deep-learning system that detects colorectal cancer specimens with dMMR or MSI using H&E-stained slides; it detected tissues with dMMR with an AUROC of 0.96 in a large, international validation cohort. This system might be used for high-throughput, low-cost evaluation of colorectal tissue specimens.

Indexed as

Deep LearningMicrosatellite InstabilityAdultBrain NeoplasmsCohort StudiesColorectal NeoplasmsDNA-Binding ProteinsFemaleHumansMaleMiddle AgedMismatch Repair Endonuclease PMS2MutL Protein Homolog 1MutS Homolog 2 ProteinNeoplastic Syndromes, HereditaryPredictive Value of TestsDNA-Binding ProteinsG-T mismatch-binding proteinMismatch Repair Endonuclease PMS2MLH1 protein, humanMSH2 protein, humanMutL Protein Homolog 1MutS Homolog 2 ProteinPMS2 protein, humanbiomarkercancer immunotherapyLynch syndromemutation

Identifiers

PMID32562722
PMCPMC7578071
OpenAlexW3036122989

What OpenQuestion holds

Textmetadata
LicenceTDM
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.