Evidence map›Paper›PMID 35247870›Full record

ArticleESMO open2022

Artificial intelligence for detection of microsatellite instability in colorectal cancer-a multicentric analysis of a pre-screening tool for clinical application.

A Echle, N Ghaffari Laleh, P Quirke, H I Grabsch, H S Muti, O L Saldanha, S F Brockmoeller, P A van den Brandt, G G A Hutchins, S D Richman and 28 more

Abstract readMulticenter Study
PubMed Full text at PMC Publisher
In one paragraph

Article in ESMO open, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 62 papers.

0numbers the graph read from it
0cells of the map it votes in
62citing papers in PubMed
–field-weighted citation impact
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

62 citing papers in PubMed.

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  8. Artificial intelligence predictsVeterinary pathology · 2026
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2 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

38 authors.

A EchleDepartment of Medicine III, University Hospital RWTH Aachen, Aachen, Germany.
N Ghaffari LalehDepartment of Medicine III, University Hospital RWTH Aachen, Aachen, Germany.
P QuirkeDivision of Pathology and Data Analytics, Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, UK.
H I GrabschDivision of 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, The Netherlands.
H S MutiDepartment of Medicine III, University Hospital RWTH Aachen, Aachen, Germany.
O L SaldanhaDepartment of Medicine III, University Hospital RWTH Aachen, Aachen, Germany.
S F BrockmoellerDivision of Pathology and Data Analytics, Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, UK.
P A van den BrandtDepartment of Epidemiology, Maastricht University Medical Center+, Maastricht, The Netherlands.
G G A HutchinsDivision of Pathology and Data Analytics, Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, UK.
S D RichmanDivision of Pathology and Data Analytics, Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, UK.
K HorisbergerDepartment of Abdominal and Transplantation Surgery, University Hospital of Zurich, Zurich, Switzerland.
C GalataDepartment of Surgery, Universitätsmedizin Mannheim, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany; Division of Thoracic Surgery, Academic Thoracic Center Mainz, University Medical Center Mainz, Johannes Gutenberg University Mainz, Mainz, Germany.
M P EbertDepartment of Medicine II, University Medical Center Mannheim, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany; Mannheim Institute for Innate Immunoscience (MI3) and Clinical Cooperation Unit Healthy Metabolism, Center of Preventive Medicine and Digital Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany; Mannheim Cancer Center, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany.
M EckardtDepartment of Medicine II, University Medical Center Mannheim, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany.
M BoutrosDivision of Signaling and Functional Genomics, German Cancer Research Center (DKFZ), Heidelberg, Germany.
D HorstInstitut für Pathologie Charité, Berlin, Germany.
C ReissfelderDepartment of Surgery, Universitätsmedizin Mannheim, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany.
E AlwersDivision of Clinical Epidemiology and Aging Research, German Cancer Research Center, Heidelberg, Germany.
T J BrinkerDigital Biomarkers for Oncology Group, National Center for Tumor Diseases (NCT), German Cancer Research Center (DKFZ), Heidelberg, Germany.
R LangerInstitute of Pathology, Inselspital, University of Bern, Bern, Switzerland.
J C A JenniskensDepartment of Epidemiology, Maastricht University Medical Center+, Maastricht, The Netherlands.
K OffermansDepartment of Epidemiology, Maastricht University Medical Center+, Maastricht, The Netherlands.
W MuellerGemeinschaftspraxis Pathologie, Starnberg, Germany.
R GrayClinical Trial Service Unit, University of Oxford, Oxford, UK.
S B GruberCenter for Precision Medicine and Department of Medical Oncology, City of Hope National Medical Center, Duarte, USA.
J K GreensonDepartment of Pathology, City of Hope Comprehensive Cancer Center, Duarte, USA.
G RennertDepartment of Community Medicine & Epidemiology, Lady Davis Carmel Medical Center, Ruth & Bruce Rappaport Faculty of Medicine, Technion-Israel Institute of Technology, Haifa, Israel; Steve and Cindy Rasmussen Institute for Genomic Medicine, Lady Davis Carmel Medical Center and Technion Faculty of Medicine, Clalit National Cancer Control Center, Haifa, Israel.
J D BonnerCenter for Precision Medicine and Department of Medical Oncology, City of Hope National Medical Center, Duarte, USA.
D SchmolzeDepartment of Pathology, City of Hope Comprehensive Cancer Center, Duarte, USA.
J Chang-ClaudeDivision of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany; Cancer Epidemiology Group, University Cancer Center Hamburg, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.
H BrennerDivision of Clinical Epidemiology and Aging Research, German Cancer Research Center, 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.
C TrautweinDepartment of Medicine III, University Hospital RWTH Aachen, Aachen, Germany.
P BoorInstitute of Pathology, University Hospital RWTH Aachen, Aachen, Germany; Department of Nephrology and Immunology, University Hospital RWTH Aachen, Aachen, Germany.
D JaegerMedical Oncology, National Center for Tumor Diseases (NCT), University Hospital Heidelberg, Heidelberg, Germany.
N T GaisaInstitute of Pathology, University Hospital RWTH Aachen, Aachen, Germany.
M HoffmeisterDivision of Clinical Epidemiology and Aging Research, German Cancer Research Center, Heidelberg, Germany.
N P WestDivision of Pathology and Data Analytics, Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, UK.
J N KatherDepartment of Medicine III, University Hospital RWTH Aachen, Aachen, Germany; Division of Pathology and Data Analytics, Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, UK; Medical Oncology, National Center for Tumor Diseases (NCT), University Hospital Heidelberg, Heidelberg, Germany. Electronic address: jkather@ukaachen.de.

Funding

MOLECULAR EPIDEMIOLOGY OF COLORECTAL CANCERR01CA081488 · NCI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI GRUBER, STEPHEN B · 1999 to 2008
$9.2M
Integration of epidemiology, pathology, immunology and outcomes in colorectal cancerR01CA263318 · NCI · BECKMAN RESEARCH INSTITUTE/CITY OF HOPE · PI STEPHEN B GRUBER · 2022 to 2026
$3.4M
The Epidemiology of Immune Responses in Colorectal CancerR01CA197350 · NCI · UNIVERSITY OF SOUTHERN CALIFORNIA · PI GRUBER, STEPHEN B · 2015 to 2019
$3.2M
NCI NIH HHS R01 CA081488NCI NIH HHS R01 CA197350NCI NIH HHS R01 CA263318
6 · The paper itself

Abstract

backgroundMicrosatellite instability (MSI)/mismatch repair deficiency (dMMR) is a key genetic feature which should be tested in every patient with colorectal cancer (CRC) according to medical guidelines. Artificial intelligence (AI) methods can detect MSI/dMMR directly in routine pathology slides, but the test performance has not been systematically investigated with predefined test thresholds.

methodWe trained and validated AI-based MSI/dMMR detectors and evaluated predefined performance metrics using nine patient cohorts of 8343 patients across different countries and ethnicities.

resultsClassifiers achieved clinical-grade performance, yielding an area under the receiver operating curve (AUROC) of up to 0.96 without using any manual annotations. Subsequently, we show that the AI system can be applied as a rule-out test: by using cohort-specific thresholds, on average 52.73% of tumors in each surgical cohort [total number of MSI/dMMR = 1020, microsatellite stable (MSS)/ proficient mismatch repair (pMMR) = 7323 patients] could be identified as MSS/pMMR with a fixed sensitivity at 95%. In an additional cohort of N = 1530 (MSI/dMMR = 211, MSS/pMMR = 1319) endoscopy biopsy samples, the system achieved an AUROC of 0.89, and the cohort-specific threshold ruled out 44.12% of tumors with a fixed sensitivity at 95%. As a more robust alternative to cohort-specific thresholds, we showed that with a fixed threshold of 0.25 for all the cohorts, we can rule-out 25.51% in surgical specimens and 6.10% in biopsies.

interpretationWhen applied in a clinical setting, this means that the AI system can rule out MSI/dMMR in a quarter (with global thresholds) or half of all CRC patients (with local fine-tuning), thereby reducing cost and turnaround time for molecular profiling.

Indexed as

Colorectal NeoplasmsMicrosatellite InstabilityArtificial IntelligenceDNA Mismatch RepairEarly Detection of CancerHumansartificial intelligencebiomarkercolorectal cancerdeep learningLynch syndromemicrosatellite instability

Identifiers

PMID35247870
PMCPMC9058894

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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.