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Klauschen F, Dippel J, Keyl P, Jurmeister P, Bockmayr M, Mock A, Buchstab O, Alber M, Ruff L, Montavon G, Müller KR. [Explainable artificial intelligence in pathology]. Pathologie (Heidelb) 2024; 45:133-139. [PMID: 38315198 DOI: 10.1007/s00292-024-01308-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 01/19/2024] [Indexed: 02/07/2024]
Abstract
With the advancements in precision medicine, the demands on pathological diagnostics have increased, requiring standardized, quantitative, and integrated assessments of histomorphological and molecular pathological data. Great hopes are placed in artificial intelligence (AI) methods, which have demonstrated the ability to analyze complex clinical, histological, and molecular data for disease classification, biomarker quantification, and prognosis estimation. This paper provides an overview of the latest developments in pathology AI, discusses the limitations, particularly concerning the black box character of AI, and describes solutions to make decision processes more transparent using methods of so-called explainable AI (XAI).
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Affiliation(s)
- Frederick Klauschen
- Pathologisches Institut, Ludwig-Maximilians-Universität München, Thalkirchner Str. 36, 80337, München, Deutschland.
- Institut für Pathologie, Charité - Universitätsmedizin Berlin, Berlin, Deutschland.
- BIFOLD - Berlin Institute for the Foundations of Learning and Data, Berlin, Deutschland.
- Deutsches Krebsforschungszentrum (DKTK/DKFZ), Partnerstandort München, München, Deutschland.
| | - Jonas Dippel
- BIFOLD - Berlin Institute for the Foundations of Learning and Data, Berlin, Deutschland
- Machine Learning Group, Fachbereich Elektrotechnik und Informatik, Technische Universität Berlin, Berlin, Deutschland
| | - Philipp Keyl
- Pathologisches Institut, Ludwig-Maximilians-Universität München, Thalkirchner Str. 36, 80337, München, Deutschland
| | - Philipp Jurmeister
- Pathologisches Institut, Ludwig-Maximilians-Universität München, Thalkirchner Str. 36, 80337, München, Deutschland
- Deutsches Krebsforschungszentrum (DKTK/DKFZ), Partnerstandort München, München, Deutschland
| | - Michael Bockmayr
- Institut für Pathologie, Charité - Universitätsmedizin Berlin, Berlin, Deutschland
- Pädiatrische Hämatologie und Onkologie, Universitätsklinikum Hamburg-Eppendorf, Hamburg, Deutschland
- Forschungsinstitut Kinderkrebs-Zentrum Hamburg, Hamburg, Deutschland
| | - Andreas Mock
- Pathologisches Institut, Ludwig-Maximilians-Universität München, Thalkirchner Str. 36, 80337, München, Deutschland
- Deutsches Krebsforschungszentrum (DKTK/DKFZ), Partnerstandort München, München, Deutschland
| | - Oliver Buchstab
- Pathologisches Institut, Ludwig-Maximilians-Universität München, Thalkirchner Str. 36, 80337, München, Deutschland
| | - Maximilian Alber
- Institut für Pathologie, Charité - Universitätsmedizin Berlin, Berlin, Deutschland
- Aignostics GmbH, Berlin, Deutschland
| | | | - Grégoire Montavon
- BIFOLD - Berlin Institute for the Foundations of Learning and Data, Berlin, Deutschland
- Machine Learning Group, Fachbereich Elektrotechnik und Informatik, Technische Universität Berlin, Berlin, Deutschland
- Fachbereich Mathematik und Informatik, Freie Universität Berlin, Berlin, Deutschland
| | - Klaus-Robert Müller
- BIFOLD - Berlin Institute for the Foundations of Learning and Data, Berlin, Deutschland.
- Machine Learning Group, Fachbereich Elektrotechnik und Informatik, Technische Universität Berlin, Berlin, Deutschland.
- Department of Artificial Intelligence, Korea University, Seoul, Südkorea.
- Max-Planck-Institut für Informatik, Saarbrücken, Deutschland.
- Machine Learning/Intelligent Data Analysis (IDA), Technische Universität Berlin, Marchstr. 23, 10587, Berlin, Deutschland.
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Klauschen F, Dippel J, Keyl P, Jurmeister P, Bockmayr M, Mock A, Buchstab O, Alber M, Ruff L, Montavon G, Müller KR. Toward Explainable Artificial Intelligence for Precision Pathology. Annu Rev Pathol 2024; 19:541-570. [PMID: 37871132 DOI: 10.1146/annurev-pathmechdis-051222-113147] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/25/2023]
Abstract
The rapid development of precision medicine in recent years has started to challenge diagnostic pathology with respect to its ability to analyze histological images and increasingly large molecular profiling data in a quantitative, integrative, and standardized way. Artificial intelligence (AI) and, more precisely, deep learning technologies have recently demonstrated the potential to facilitate complex data analysis tasks, including clinical, histological, and molecular data for disease classification; tissue biomarker quantification; and clinical outcome prediction. This review provides a general introduction to AI and describes recent developments with a focus on applications in diagnostic pathology and beyond. We explain limitations including the black-box character of conventional AI and describe solutions to make machine learning decisions more transparent with so-called explainable AI. The purpose of the review is to foster a mutual understanding of both the biomedical and the AI side. To that end, in addition to providing an overview of the relevant foundations in pathology and machine learning, we present worked-through examples for a better practical understanding of what AI can achieve and how it should be done.
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Affiliation(s)
- Frederick Klauschen
- Institute of Pathology, Ludwig-Maximilians-Universität München, Munich, Germany;
- Institute of Pathology, Charité Universitätsmedizin Berlin, Berlin, Germany
- Berlin Institute for the Foundations of Learning and Data (BIFOLD), Berlin, Germany
- German Cancer Consortium, German Cancer Research Center (DKTK/DKFZ), Munich Partner Site, Munich, Germany
| | - Jonas Dippel
- Berlin Institute for the Foundations of Learning and Data (BIFOLD), Berlin, Germany
- Machine Learning Group, Department of Electrical Engineering and Computer Science, Technische Universität Berlin, Berlin, Germany;
| | - Philipp Keyl
- Institute of Pathology, Ludwig-Maximilians-Universität München, Munich, Germany;
| | - Philipp Jurmeister
- Institute of Pathology, Ludwig-Maximilians-Universität München, Munich, Germany;
- German Cancer Consortium, German Cancer Research Center (DKTK/DKFZ), Munich Partner Site, Munich, Germany
| | - Michael Bockmayr
- Institute of Pathology, Charité Universitätsmedizin Berlin, Berlin, Germany
- Department of Pediatric Hematology and Oncology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Research Institute Children's Cancer Center Hamburg, Hamburg, Germany
| | - Andreas Mock
- Institute of Pathology, Ludwig-Maximilians-Universität München, Munich, Germany;
- German Cancer Consortium, German Cancer Research Center (DKTK/DKFZ), Munich Partner Site, Munich, Germany
| | - Oliver Buchstab
- Institute of Pathology, Ludwig-Maximilians-Universität München, Munich, Germany;
| | - Maximilian Alber
- Institute of Pathology, Charité Universitätsmedizin Berlin, Berlin, Germany
- Aignostics, Berlin, Germany
| | | | - Grégoire Montavon
- Berlin Institute for the Foundations of Learning and Data (BIFOLD), Berlin, Germany
- Machine Learning Group, Department of Electrical Engineering and Computer Science, Technische Universität Berlin, Berlin, Germany;
- Department of Mathematics and Computer Science, Freie Universität Berlin, Berlin, Germany
| | - Klaus-Robert Müller
- Berlin Institute for the Foundations of Learning and Data (BIFOLD), Berlin, Germany
- Machine Learning Group, Department of Electrical Engineering and Computer Science, Technische Universität Berlin, Berlin, Germany;
- Department of Artificial Intelligence, Korea University, Seoul, Korea
- Max Planck Institute for Informatics, Saarbrücken, Germany
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3
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Keyl J, Bucher A, Jungmann F, Hosch R, Ziller A, Armbruster R, Malkomes P, Reissig TM, Koitka S, Tzianopoulos I, Keyl P, Kostbade K, Albers D, Markus P, Treckmann J, Nassenstein K, Haubold J, Makowski M, Forsting M, Baba HA, Kasper S, Siveke JT, Nensa F, Schuler M, Kaissis G, Kleesiek J, Braren R. Prognostic value of deep learning-derived body composition in advanced pancreatic cancer-a retrospective multicenter study. ESMO Open 2024; 9:102219. [PMID: 38194881 PMCID: PMC10837775 DOI: 10.1016/j.esmoop.2023.102219] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2023] [Revised: 12/11/2023] [Accepted: 12/13/2023] [Indexed: 01/11/2024] Open
Abstract
BACKGROUND Despite the prognostic relevance of cachexia in pancreatic cancer, individual body composition has not been routinely integrated into treatment planning. In this multicenter study, we investigated the prognostic value of sarcopenia and myosteatosis automatically extracted from routine computed tomography (CT) scans of patients with advanced pancreatic ductal adenocarcinoma (PDAC). PATIENTS AND METHODS We retrospectively analyzed clinical imaging data of 601 patients from three German cancer centers. We applied a deep learning approach to assess sarcopenia by the abdominal muscle-to-bone ratio (MBR) and myosteatosis by the ratio of abdominal inter- and intramuscular fat to muscle volume. In the pooled cohort, univariable and multivariable analyses were carried out to analyze the association between body composition markers and overall survival (OS). We analyzed the relationship between body composition markers and laboratory values during the first year of therapy in a subgroup using linear regression analysis adjusted for age, sex, and American Joint Committee on Cancer (AJCC) stage. RESULTS Deep learning-derived MBR [hazard ratio (HR) 0.60, 95% confidence interval (CI) 0.47-0.77, P < 0.005] and myosteatosis (HR 3.73, 95% CI 1.66-8.39, P < 0.005) were significantly associated with OS in univariable analysis. In multivariable analysis, MBR (P = 0.019) and myosteatosis (P = 0.02) were associated with OS independent of age, sex, and AJCC stage. In a subgroup, MBR and myosteatosis were associated with albumin and C-reactive protein levels after initiation of therapy. Additionally, MBR was also associated with hemoglobin and total protein levels. CONCLUSIONS Our work demonstrates that deep learning can be applied across cancer centers to automatically assess sarcopenia and myosteatosis from routine CT scans. We highlight the prognostic role of our proposed markers and show a strong relationship with protein levels, inflammation, and anemia. In clinical practice, automated body composition analysis holds the potential to further personalize cancer treatment.
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Affiliation(s)
- J Keyl
- Institute for Artificial Intelligence in Medicine, University Hospital Essen (AöR), Essen, Germany; Institute of Pathology, University Hospital Essen (AöR), Essen, Germany.
| | - A Bucher
- Institute for Diagnostic and Interventional Radiology, Goethe University Frankfurt, Frankfurt am Main, Germany; German Cancer Consortium (DKTK), Frankfurt partner site, Heidelberg, Germany
| | - F Jungmann
- Institute of Diagnostic and Interventional Radiology, Technical University of Munich, School of Medicine, Munich, Germany; Artificial Intelligence in Healthcare and Medicine, School of Computation, Information and Technology, Technical University of Munich, Munich, Germany
| | - R Hosch
- Institute for Artificial Intelligence in Medicine, University Hospital Essen (AöR), Essen, Germany
| | - A Ziller
- Institute of Diagnostic and Interventional Radiology, Technical University of Munich, School of Medicine, Munich, Germany; Artificial Intelligence in Healthcare and Medicine, School of Computation, Information and Technology, Technical University of Munich, Munich, Germany
| | - R Armbruster
- Institute for Diagnostic and Interventional Radiology, Goethe University Frankfurt, Frankfurt am Main, Germany
| | - P Malkomes
- Department of General, Visceral and Transplant Surgery, Goethe University Hospital Frankfurt, Frankfurt am Main, Germany
| | - T M Reissig
- Department of Medical Oncology, University Hospital Essen (AöR), Essen, Germany; West German Cancer Center, University Hospital Essen (AöR), Essen, Germany; German Cancer Consortium (DKTK), Partner site University Hospital Essen (AöR), Essen, Germany; Bridge Institute of Experimental Tumor Therapy, West German Cancer Center, University Hospital Essen, University of Duisburg-Essen, Essen, Germany; Division of Solid Tumor Translational Oncology, German Cancer Consortium (DKTK Partner Site Essen) and German Cancer Research Center, DKFZ, Heidelberg, Germany
| | - S Koitka
- Institute for Artificial Intelligence in Medicine, University Hospital Essen (AöR), Essen, Germany; Institute for Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen (AöR), Essen, Germany
| | - I Tzianopoulos
- Department of Medical Oncology, University Hospital Essen (AöR), Essen, Germany; West German Cancer Center, University Hospital Essen (AöR), Essen, Germany; Bridge Institute of Experimental Tumor Therapy, West German Cancer Center, University Hospital Essen, University of Duisburg-Essen, Essen, Germany; Division of Solid Tumor Translational Oncology, German Cancer Consortium (DKTK Partner Site Essen) and German Cancer Research Center, DKFZ, Heidelberg, Germany
| | - P Keyl
- Institute of Pathology, Ludwig-Maximilians-University Munich, Munich, Germany
| | - K Kostbade
- Department of Medical Oncology, University Hospital Essen (AöR), Essen, Germany; West German Cancer Center, University Hospital Essen (AöR), Essen, Germany; Medical Faculty, University of Duisburg-Essen, Essen, Germany
| | - D Albers
- Department of Gastroenterology, Elisabeth Hospital Essen, Essen, Germany
| | - P Markus
- Department of General Surgery and Traumatology, Elisabeth Hospital Essen, Essen, Germany
| | - J Treckmann
- West German Cancer Center, University Hospital Essen (AöR), Essen, Germany; Medical Faculty, University of Duisburg-Essen, Essen, Germany; Department of General, Visceral and Transplant Surgery, University Hospital Essen, Essen, Germany
| | - K Nassenstein
- Institute for Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen (AöR), Essen, Germany; Medical Faculty, University of Duisburg-Essen, Essen, Germany
| | - J Haubold
- Institute for Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen (AöR), Essen, Germany; Medical Faculty, University of Duisburg-Essen, Essen, Germany
| | - M Makowski
- Institute of Diagnostic and Interventional Radiology, Technical University of Munich, School of Medicine, Munich, Germany
| | - M Forsting
- German Cancer Consortium (DKTK), Partner site University Hospital Essen (AöR), Essen, Germany; Institute for Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen (AöR), Essen, Germany; Medical Faculty, University of Duisburg-Essen, Essen, Germany
| | - H A Baba
- Institute of Pathology, University Hospital Essen (AöR), Essen, Germany; Medical Faculty, University of Duisburg-Essen, Essen, Germany
| | - S Kasper
- Department of Medical Oncology, University Hospital Essen (AöR), Essen, Germany; West German Cancer Center, University Hospital Essen (AöR), Essen, Germany; German Cancer Consortium (DKTK), Partner site University Hospital Essen (AöR), Essen, Germany; Medical Faculty, University of Duisburg-Essen, Essen, Germany
| | - J T Siveke
- Department of Medical Oncology, University Hospital Essen (AöR), Essen, Germany; West German Cancer Center, University Hospital Essen (AöR), Essen, Germany; German Cancer Consortium (DKTK), Partner site University Hospital Essen (AöR), Essen, Germany; Bridge Institute of Experimental Tumor Therapy, West German Cancer Center, University Hospital Essen, University of Duisburg-Essen, Essen, Germany; Division of Solid Tumor Translational Oncology, German Cancer Consortium (DKTK Partner Site Essen) and German Cancer Research Center, DKFZ, Heidelberg, Germany; Medical Faculty, University of Duisburg-Essen, Essen, Germany
| | - F Nensa
- Institute for Artificial Intelligence in Medicine, University Hospital Essen (AöR), Essen, Germany; German Cancer Consortium (DKTK), Partner site University Hospital Essen (AöR), Essen, Germany; Institute for Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen (AöR), Essen, Germany; Medical Faculty, University of Duisburg-Essen, Essen, Germany
| | - M Schuler
- Department of Medical Oncology, University Hospital Essen (AöR), Essen, Germany; West German Cancer Center, University Hospital Essen (AöR), Essen, Germany; Medical Faculty, University of Duisburg-Essen, Essen, Germany; National Center for Tumor Diseases (NCT), NCT West, Essen, Germany
| | - G Kaissis
- Institute of Diagnostic and Interventional Radiology, Technical University of Munich, School of Medicine, Munich, Germany; Artificial Intelligence in Healthcare and Medicine, School of Computation, Information and Technology, Technical University of Munich, Munich, Germany
| | - J Kleesiek
- Institute for Artificial Intelligence in Medicine, University Hospital Essen (AöR), Essen, Germany; West German Cancer Center, University Hospital Essen (AöR), Essen, Germany; German Cancer Consortium (DKTK), Partner site University Hospital Essen (AöR), Essen, Germany; Medical Faculty, University of Duisburg-Essen, Essen, Germany
| | - R Braren
- Institute of Diagnostic and Interventional Radiology, Technical University of Munich, School of Medicine, Munich, Germany; German Cancer Consortium (DKTK), Munich partner site, Heidelberg, Germany
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4
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Keyl P, Bischoff P, Dernbach G, Bockmayr M, Fritz R, Horst D, Blüthgen N, Montavon G, Müller KR, Klauschen F. Single-cell gene regulatory network prediction by explainable AI. Nucleic Acids Res 2023; 51:e20. [PMID: 36629274 PMCID: PMC9976884 DOI: 10.1093/nar/gkac1212] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2022] [Revised: 11/16/2022] [Accepted: 12/06/2022] [Indexed: 01/12/2023] Open
Abstract
The molecular heterogeneity of cancer cells contributes to the often partial response to targeted therapies and relapse of disease due to the escape of resistant cell populations. While single-cell sequencing has started to improve our understanding of this heterogeneity, it offers a mostly descriptive view on cellular types and states. To obtain more functional insights, we propose scGeneRAI, an explainable deep learning approach that uses layer-wise relevance propagation (LRP) to infer gene regulatory networks from static single-cell RNA sequencing data for individual cells. We benchmark our method with synthetic data and apply it to single-cell RNA sequencing data of a cohort of human lung cancers. From the predicted single-cell networks our approach reveals characteristic network patterns for tumor cells and normal epithelial cells and identifies subnetworks that are observed only in (subgroups of) tumor cells of certain patients. While current state-of-the-art methods are limited by their ability to only predict average networks for cell populations, our approach facilitates the reconstruction of networks down to the level of single cells which can be utilized to characterize the heterogeneity of gene regulation within and across tumors.
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Affiliation(s)
- Philipp Keyl
- Institute of Pathology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität Berlin, Charitéplatz 1, 10117 Berlin, Germany
| | - Philip Bischoff
- Institute of Pathology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität Berlin, Charitéplatz 1, 10117 Berlin, Germany.,Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Anna-Louisa-Karsch-Straße 2, 10178 Berlin, Germany.,German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Berlin partner site, Germany
| | - Gabriel Dernbach
- Institute of Pathology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität Berlin, Charitéplatz 1, 10117 Berlin, Germany.,BIFOLD - Berlin Institute for the Foundations of Learning and Data, Berlin, Germany
| | - Michael Bockmayr
- Institute of Pathology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität Berlin, Charitéplatz 1, 10117 Berlin, Germany.,Department of Pediatric Hematology and Oncolog, University Medical Center Hamburg-Eppendorf, Martinistr. 52, 20246 Hamburg, Germany.,Mildred Scheel Cancer Career Center HaTriCS4, University Medical Center Hamburg-Eppendorf Martinistr. 52, 20246 Hamburg, Germany
| | - Rebecca Fritz
- Institute of Pathology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität Berlin, Charitéplatz 1, 10117 Berlin, Germany
| | - David Horst
- Institute of Pathology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität Berlin, Charitéplatz 1, 10117 Berlin, Germany.,German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Berlin partner site, Germany
| | - Nils Blüthgen
- Institute of Pathology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität Berlin, Charitéplatz 1, 10117 Berlin, Germany.,Institut für Biologie, Humboldt University, Free University of Berlin, Unter den Linden 6, 10099 Berlin, Germany
| | - Grégoire Montavon
- BIFOLD - Berlin Institute for the Foundations of Learning and Data, Berlin, Germany.,Machine Learning Group, Technical University of Berlin, Marchstr. 23, 10587 Berlin, Germany
| | - Klaus-Robert Müller
- BIFOLD - Berlin Institute for the Foundations of Learning and Data, Berlin, Germany.,Machine Learning Group, Technical University of Berlin, Marchstr. 23, 10587 Berlin, Germany.,Department of Artificial Intelligence, Korea University, Seoul 136-713, South Korea.,Max-Planck-Institute for Informatics, Stuhlsatzenhausweg 4, 66123 Saarbrücken, Germany
| | - Frederick Klauschen
- Institute of Pathology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität Berlin, Charitéplatz 1, 10117 Berlin, Germany.,German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Berlin partner site, Germany.,BIFOLD - Berlin Institute for the Foundations of Learning and Data, Berlin, Germany.,Institute of Pathology, Ludwig-Maximilians-University Munich, Thalkirchner Str. 36, 80337 München, Germany.,German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Munich partner site, Germany
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5
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Jurmeister P, Glöß S, Roller R, Leitheiser M, Schmid S, Mochmann LH, Payá Capilla E, Fritz R, Dittmayer C, Friedrich C, Thieme A, Keyl P, Jarosch A, Schallenberg S, Bläker H, Hoffmann I, Vollbrecht C, Lehmann A, Hummel M, Heim D, Haji M, Harter P, Englert B, Frank S, Hench J, Paulus W, Hasselblatt M, Hartmann W, Dohmen H, Keber U, Jank P, Denkert C, Stadelmann C, Bremmer F, Richter A, Wefers A, Ribbat-Idel J, Perner S, Idel C, Chiariotti L, Della Monica R, Marinelli A, Schüller U, Bockmayr M, Liu J, Lund VJ, Forster M, Lechner M, Lorenzo-Guerra SL, Hermsen M, Johann PD, Agaimy A, Seegerer P, Koch A, Heppner F, Pfister SM, Jones DTW, Sill M, von Deimling A, Snuderl M, Müller KR, Forgó E, Howitt BE, Mertins P, Klauschen F, Capper D. DNA methylation-based classification of sinonasal tumors. Nat Commun 2022; 13:7148. [PMID: 36443295 PMCID: PMC9705411 DOI: 10.1038/s41467-022-34815-3] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Accepted: 11/07/2022] [Indexed: 11/29/2022] Open
Abstract
The diagnosis of sinonasal tumors is challenging due to a heterogeneous spectrum of various differential diagnoses as well as poorly defined, disputed entities such as sinonasal undifferentiated carcinomas (SNUCs). In this study, we apply a machine learning algorithm based on DNA methylation patterns to classify sinonasal tumors with clinical-grade reliability. We further show that sinonasal tumors with SNUC morphology are not as undifferentiated as their current terminology suggests but rather reassigned to four distinct molecular classes defined by epigenetic, mutational and proteomic profiles. This includes two classes with neuroendocrine differentiation, characterized by IDH2 or SMARCA4/ARID1A mutations with an overall favorable clinical course, one class composed of highly aggressive SMARCB1-deficient carcinomas and another class with tumors that represent potentially previously misclassified adenoid cystic carcinomas. Our findings can aid in improving the diagnostic classification of sinonasal tumors and could help to change the current perception of SNUCs.
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Affiliation(s)
- Philipp Jurmeister
- grid.411095.80000 0004 0477 2585Institute of Pathology, Ludwig Maximilians University Hospital Munich, Munich, Germany ,grid.6363.00000 0001 2218 4662Institute of Pathology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin, Germany ,grid.7497.d0000 0004 0492 0584German Cancer Consortium (DKTK), Partner Site Berlin, and German Cancer Research Center (DKFZ), Heidelberg, Germany ,grid.7497.d0000 0004 0492 0584 German Cancer Consortium (DKTK), Partner Site Munich, and German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Stefanie Glöß
- grid.6363.00000 0001 2218 4662Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Neuropathology, Charitéplatz 1, Berlin, Germany
| | - Renée Roller
- grid.6363.00000 0001 2218 4662Institute of Pathology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin, Germany ,grid.7497.d0000 0004 0492 0584German Cancer Consortium (DKTK), Partner Site Berlin, and German Cancer Research Center (DKFZ), Heidelberg, Germany ,grid.484013.a0000 0004 6879 971XProteomics Platform, Berlin Institute of Health (BIH) and Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), 13125 Berlin, Germany
| | - Maximilian Leitheiser
- grid.6363.00000 0001 2218 4662Institute of Pathology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Simone Schmid
- grid.7497.d0000 0004 0492 0584German Cancer Consortium (DKTK), Partner Site Berlin, and German Cancer Research Center (DKFZ), Heidelberg, Germany ,grid.6363.00000 0001 2218 4662Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Neuropathology, Charitéplatz 1, Berlin, Germany
| | - Liliana H. Mochmann
- grid.411095.80000 0004 0477 2585Institute of Pathology, Ludwig Maximilians University Hospital Munich, Munich, Germany
| | - Emma Payá Capilla
- grid.411095.80000 0004 0477 2585Institute of Pathology, Ludwig Maximilians University Hospital Munich, Munich, Germany
| | - Rebecca Fritz
- grid.6363.00000 0001 2218 4662Institute of Pathology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Carsten Dittmayer
- grid.6363.00000 0001 2218 4662Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Neuropathology, Charitéplatz 1, Berlin, Germany
| | - Corinna Friedrich
- grid.6363.00000 0001 2218 4662Institute of Pathology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin, Germany ,grid.7497.d0000 0004 0492 0584German Cancer Consortium (DKTK), Partner Site Berlin, and German Cancer Research Center (DKFZ), Heidelberg, Germany ,grid.419491.00000 0001 1014 0849MDC Graduate School, Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany ,grid.7468.d0000 0001 2248 7639Humboldt Universität zu Berlin, Institute of Chemistry, Berlin, Germany
| | - Anne Thieme
- grid.7497.d0000 0004 0492 0584German Cancer Consortium (DKTK), Partner Site Berlin, and German Cancer Research Center (DKFZ), Heidelberg, Germany ,grid.6363.00000 0001 2218 4662Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Neuropathology, Charitéplatz 1, Berlin, Germany
| | - Philipp Keyl
- grid.6363.00000 0001 2218 4662Institute of Pathology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Armin Jarosch
- grid.6363.00000 0001 2218 4662Institute of Pathology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Simon Schallenberg
- grid.6363.00000 0001 2218 4662Institute of Pathology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Hendrik Bläker
- grid.411339.d0000 0000 8517 9062Institute of Pathology, University Hospital Leipzig, Leipzig, Germany
| | - Inga Hoffmann
- grid.6363.00000 0001 2218 4662Institute of Pathology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Claudia Vollbrecht
- grid.6363.00000 0001 2218 4662Institute of Pathology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin, Germany ,grid.7497.d0000 0004 0492 0584German Cancer Consortium (DKTK), Partner Site Berlin, and German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Annika Lehmann
- grid.6363.00000 0001 2218 4662Institute of Pathology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Michael Hummel
- grid.6363.00000 0001 2218 4662Institute of Pathology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin, Germany ,grid.7497.d0000 0004 0492 0584German Cancer Consortium (DKTK), Partner Site Berlin, and German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Daniel Heim
- grid.6363.00000 0001 2218 4662Institute of Pathology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Mohamed Haji
- grid.484013.a0000 0004 6879 971XProteomics Platform, Berlin Institute of Health (BIH) and Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), 13125 Berlin, Germany
| | - Patrick Harter
- grid.7497.d0000 0004 0492 0584 German Cancer Consortium (DKTK), Partner Site Munich, and German Cancer Research Center (DKFZ), Heidelberg, Germany ,grid.7839.50000 0004 1936 9721Institute of Neurology (Edinger Institute), Goethe-University Frankfurt am Main, Frankfurt am Main, Germany ,grid.7497.d0000 0004 0492 0584German Cancer Consortium (DKTK), Partner Site Frankfurt/Mainz, Frankfurt am Main, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Benjamin Englert
- grid.411095.80000 0004 0477 2585Institute of Neuropathology, Ludwig Maximilians University Hospital Munich, Munich, Germany
| | - Stephan Frank
- grid.410567.1Department of Neuropathology, Institute of Pathology, Basel University Hospital, Basel, Switzerland
| | - Jürgen Hench
- grid.410567.1Department of Neuropathology, Institute of Pathology, Basel University Hospital, Basel, Switzerland
| | - Werner Paulus
- grid.16149.3b0000 0004 0551 4246Institute of Neuropathology, University Hospital Münster, Münster, Germany
| | - Martin Hasselblatt
- grid.16149.3b0000 0004 0551 4246Institute of Neuropathology, University Hospital Münster, Münster, Germany
| | - Wolfgang Hartmann
- grid.16149.3b0000 0004 0551 4246Division of Translational Pathology, Gerhard-Domagk-Institute of Pathology, University Hospital Münster, Münster, Germany
| | - Hildegard Dohmen
- grid.8664.c0000 0001 2165 8627Institute of Neuropathology, University of Giessen, Giessen, Germany
| | - Ursula Keber
- grid.10253.350000 0004 1936 9756Institute of Neuropathology, Philipps-University, Marburg, Germany
| | - Paul Jank
- grid.10253.350000 0004 1936 9756Institute of Pathology, Philipps-University Marburg and University Hospital Marburg, Marburg, Germany
| | - Carsten Denkert
- grid.10253.350000 0004 1936 9756Institute of Pathology, Philipps-University Marburg and University Hospital Marburg, Marburg, Germany
| | - Christine Stadelmann
- grid.411984.10000 0001 0482 5331Institute for Neuropathology, University Medical Centre Göttingen, Göttingen, Germany
| | - Felix Bremmer
- grid.411984.10000 0001 0482 5331Institute of Pathology, University Medical Center, Göttingen, Germany
| | - Annika Richter
- grid.411984.10000 0001 0482 5331Institute of Pathology, University Medical Center, Göttingen, Germany
| | - Annika Wefers
- grid.5253.10000 0001 0328 4908Department of Neuropathology, University Hospital Heidelberg, Heidelberg, Germany ,grid.7497.d0000 0004 0492 0584Clinical Cooperation Unit Neuropathology, German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany ,grid.13648.380000 0001 2180 3484Institute of Neuropathology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Julika Ribbat-Idel
- Institute of Pathology, University of Luebeck and University Hospital Schleswig-Holstein, Campus Luebeck, Luebeck, Germany
| | - Sven Perner
- Institute of Pathology, University of Luebeck and University Hospital Schleswig-Holstein, Campus Luebeck, Luebeck, Germany ,grid.418187.30000 0004 0493 9170Pathology, Research Center Borstel, Leibniz Lung Center, Borstel, Germany ,grid.452624.3German Center for Lung Research (DZL), Partner Site Luebeck, Luebeck, Germany
| | - Christian Idel
- grid.412468.d0000 0004 0646 2097Department of Otorhinolaryngology, University Hospital Schleswig-Holstein, Campus Lübeck, Lübeck, Germany
| | - Lorenzo Chiariotti
- grid.4691.a0000 0001 0790 385XDipartimento di Medicina Molecolare e Biotecnologie Mediche, Università degli Studi di Napoli Federico II, Via S. Pansini 5, 80131 Naples, Italy ,grid.4691.a0000 0001 0790 385XCEINGE Biotecnologie Avanzate, 80145 Naples, Italy
| | - Rosa Della Monica
- grid.4691.a0000 0001 0790 385XCEINGE Biotecnologie Avanzate, 80145 Naples, Italy
| | - Alfredo Marinelli
- grid.4691.a0000 0001 0790 385XDepartment of Medicina Clinica e Chirurgia, University Federico II, Naples, Italy
| | - Ulrich Schüller
- grid.13648.380000 0001 2180 3484Institute of Neuropathology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany ,grid.13648.380000 0001 2180 3484Department of Pediatric Hematology and Oncology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany ,grid.470174.1Research Institute Children’s Cancer Center Hamburg, Hamburg, Germany
| | - Michael Bockmayr
- grid.6363.00000 0001 2218 4662Institute of Pathology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin, Germany ,grid.13648.380000 0001 2180 3484Department of Pediatric Hematology and Oncology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany ,grid.470174.1Research Institute Children’s Cancer Center Hamburg, Hamburg, Germany
| | - Jacklyn Liu
- grid.83440.3b0000000121901201UCL Cancer Institute, University College London, 72 Huntley Street, London, WC1E 6BT UK ,grid.83440.3b0000000121901201UCL Academic Head and Neck Centre, Division of Surgery and Interventional Science, University College London, London, UK
| | - Valerie J. Lund
- grid.83440.3b0000000121901201UCL Cancer Institute, University College London, 72 Huntley Street, London, WC1E 6BT UK ,grid.83440.3b0000000121901201UCL Academic Head and Neck Centre, Division of Surgery and Interventional Science, University College London, London, UK
| | - Martin Forster
- grid.83440.3b0000000121901201UCL Cancer Institute, University College London, 72 Huntley Street, London, WC1E 6BT UK ,grid.83440.3b0000000121901201UCL Academic Head and Neck Centre, Division of Surgery and Interventional Science, University College London, London, UK
| | - Matt Lechner
- grid.83440.3b0000000121901201UCL Cancer Institute, University College London, 72 Huntley Street, London, WC1E 6BT UK ,grid.83440.3b0000000121901201UCL Academic Head and Neck Centre, Division of Surgery and Interventional Science, University College London, London, UK
| | - Sara L. Lorenzo-Guerra
- grid.511562.4Department of Head and Neck Oncology, Instituto de Investigación Sanitaria del Principado de Asturias (ISPA), Oviedo, Spain
| | - Mario Hermsen
- grid.511562.4Department of Head and Neck Oncology, Instituto de Investigación Sanitaria del Principado de Asturias (ISPA), Oviedo, Spain
| | - Pascal D. Johann
- Swabian Childrens’ Cancer Center, University Childrens’ Hospital Augsburg and EU-RHAB Registry, Augsburg, Germany
| | - Abbas Agaimy
- grid.411668.c0000 0000 9935 6525Institute of Pathology, Friedrich-Alexander-University Erlangen-Nürnberg, University Hospital, Erlangen, Germany
| | - Philipp Seegerer
- grid.6734.60000 0001 2292 8254Machine-Learning Group, Department of Software Engineering and Theoretical Computer Science, Technical University of Berlin, Berlin, Germany
| | - Arend Koch
- grid.6363.00000 0001 2218 4662Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Neuropathology, Charitéplatz 1, Berlin, Germany
| | - Frank Heppner
- grid.7497.d0000 0004 0492 0584German Cancer Consortium (DKTK), Partner Site Berlin, and German Cancer Research Center (DKFZ), Heidelberg, Germany ,grid.6363.00000 0001 2218 4662Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Neuropathology, Charitéplatz 1, Berlin, Germany
| | - Stefan M. Pfister
- grid.510964.fHopp Children’s Cancer Center Heidelberg (KiTZ), Heidelberg, Germany ,grid.7497.d0000 0004 0492 0584Division of Pediatric Neurooncology, German Cancer Research Center (DKFZ) and German Cancer Consortium (DKTK), Heidelberg, Germany ,grid.5253.10000 0001 0328 4908Department of Hematology and Oncology, Heidelberg University Hospital, Heidelberg, Germany
| | - David T. W. Jones
- grid.510964.fHopp Children’s Cancer Center Heidelberg (KiTZ), Heidelberg, Germany ,grid.7497.d0000 0004 0492 0584Division of Pediatric Glioma Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Martin Sill
- grid.510964.fHopp Children’s Cancer Center Heidelberg (KiTZ), Heidelberg, Germany
| | - Andreas von Deimling
- grid.5253.10000 0001 0328 4908Department of Neuropathology, University Hospital Heidelberg, Heidelberg, Germany ,grid.7497.d0000 0004 0492 0584Clinical Cooperation Unit Neuropathology, German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Matija Snuderl
- grid.240324.30000 0001 2109 4251Division of Neuropathology, NYU Langone Health, New York, USA ,grid.240324.30000 0001 2109 4251Laura and Isaac Perlmutter Cancer Center, NYU Langone Health, New York, USA ,grid.240324.30000 0001 2109 4251Division of Molecular Pathology and Diagnostics, NYU Langone Health, New York, USA
| | - Klaus-Robert Müller
- grid.6734.60000 0001 2292 8254Machine-Learning Group, Department of Software Engineering and Theoretical Computer Science, Technical University of Berlin, Berlin, Germany ,grid.222754.40000 0001 0840 2678Department of Artificial Intelligence, Korea University, Seoul, South Korea ,grid.419528.30000 0004 0491 9823Max-Planck-Institute for Informatics, Saarbrücken, Germany ,BIFOLD – Berlin Institute for the Foundations of Learning and Data, Berlin, Germany
| | - Erna Forgó
- grid.168010.e0000000419368956Stanford University School of Medicine, Stanford, CA USA
| | - Brooke E. Howitt
- grid.168010.e0000000419368956Stanford University School of Medicine, Stanford, CA USA
| | - Philipp Mertins
- grid.484013.a0000 0004 6879 971XProteomics Platform, Berlin Institute of Health (BIH) and Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), 13125 Berlin, Germany
| | - Frederick Klauschen
- grid.411095.80000 0004 0477 2585Institute of Pathology, Ludwig Maximilians University Hospital Munich, Munich, Germany ,grid.7497.d0000 0004 0492 0584 German Cancer Consortium (DKTK), Partner Site Munich, and German Cancer Research Center (DKFZ), Heidelberg, Germany ,BIFOLD – Berlin Institute for the Foundations of Learning and Data, Berlin, Germany
| | - David Capper
- grid.7497.d0000 0004 0492 0584German Cancer Consortium (DKTK), Partner Site Berlin, and German Cancer Research Center (DKFZ), Heidelberg, Germany ,grid.6363.00000 0001 2218 4662Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Neuropathology, Charitéplatz 1, Berlin, Germany
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6
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Keyl P, Schneiders M, Schuld C, Franz S, Hommelsen M, Weidner N, Rupp R. Differences in Characteristics of Error-Related Potentials Between Individuals With Spinal Cord Injury and Age- and Sex-Matched Able-Bodied Controls. Front Neurol 2019; 9:1192. [PMID: 30766510 PMCID: PMC6365444 DOI: 10.3389/fneur.2018.01192] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2018] [Accepted: 12/27/2018] [Indexed: 12/25/2022] Open
Abstract
Background: Non-invasive brain-computer interfaces (BCI) represent an emerging technology for enabling persons with impaired or lost grasping and reaching functions due to high spinal cord injury (SCI) to control assistive devices. A major drawback of BCIs is a high rate of false classifications. The robustness and performance of BCIs might be improved using cerebral electrophysiological correlates of error recognition (error-related potentials, ErrPs). As ErrPs have never been systematically examined in subjects with SCI, this study compares the characteristics of ErrPs in individuals with SCI with those of able-bodied control subjects. Methods: ErrPs at FCz and Cz were analyzed in 11 subjects with SCI (9 male, median age 28 y) and in 11 sex- and age-matched controls. Moving a shoulder joystick according to a visual cue, subjects received feedback about the match/mismatch of the performed movement. ErrPs occurring after "error"-feedback were evaluated by comparing means of voltage values within three consecutive time windows after feedback (wP1, wN1, wP2 containing peak voltages P1, N1, P2) using repeated-measurement analysis of variance. Results: In the control group, mean voltage values for the "error" and "correct" feedback condition differed significantly around N1 (FCz: 254 ms, Cz: 252 ms) and P2 (FCz: 347 ms, Cz: 345 ms), but not around P1 (FCz: 181 ms, Cz: 179 ms). ErrPs of the control and the SCI group showed similar morphology, however mean amplitudes of ErrPs were significantly smaller in individuals with SCI compared to controls for wN1 (FCz: control = -1.55 μV, SCI = -0.27 μV, p = 0.02; Cz: control = -1.03 μV, SCI = 0.11 μV, p = 0.04) and wP2 (FCz: control = 2.79 μV, SCI = 1.29 μV, p = 0.011; Cz: control = 2.12 μV, SCI = 0.81 μV, p = 0.003). Mean voltage values in wP1, wN1, and wP2 did not correlate significantly with either chronicity after or level of injury. Conclusion: The morphology of ErrPs in subjects with and without SCI is comparable, however, with reduced mean amplitude in wN1 and wP2 in the SCI group. Further studies should evaluate whether ErrP-classification can be used for online correction of false BCI-commands in individuals with SCI.
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Affiliation(s)
- Philipp Keyl
- Spinal Cord Injury Center, Heidelberg University Hospital, Heidelberg, Germany
| | - Matthias Schneiders
- Spinal Cord Injury Center, Heidelberg University Hospital, Heidelberg, Germany
| | - Christian Schuld
- Spinal Cord Injury Center, Heidelberg University Hospital, Heidelberg, Germany
| | - Steffen Franz
- Spinal Cord Injury Center, Heidelberg University Hospital, Heidelberg, Germany
| | | | - Nobert Weidner
- Spinal Cord Injury Center, Heidelberg University Hospital, Heidelberg, Germany
| | - Rüdiger Rupp
- Spinal Cord Injury Center, Heidelberg University Hospital, Heidelberg, Germany
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7
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Hommelsen M, Schneiders M, Schuld C, Keyl P, Rupp R. Sensory Feedback Interferes with Mu Rhythm Based Detection of Motor Commands from Electroencephalographic Signals. Front Hum Neurosci 2017; 11:523. [PMID: 29163103 PMCID: PMC5672058 DOI: 10.3389/fnhum.2017.00523] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2017] [Accepted: 10/16/2017] [Indexed: 11/13/2022] Open
Abstract
Background: Electroencephalogram (EEG)-based brain-computer interfaces (BCI) represent a promising component of restorative motor therapies in individuals with partial paralysis. However, in those patients, sensory functions such as proprioception are at least partly preserved. The aim of this study was to investigate whether afferent feedback interferes with the BCI-based detection of efferent motor commands during execution of movements. Methods: Brain activity of 13 able-bodied subjects (age: 29.1 ± 4.8 years; 11 males) was compared between a motor task (MT) consisting of an isometric, isotonic grip and a somatosensory electrical stimulation (SS) of the fingertips. Modulation of the mu rhythm (8-13 Hz) was investigated to identify changes specifically related to the generation of efferent commands. A linear discriminant analysis (LDA) was used to investigate the activation pattern on a single-trial basis. Classifiers were trained with MT vs. REST (periods without MT/SS) and tested with SS and vice versa to quantify the impact of afferent feedback on the classification results. Results: Few differences in the spatial pattern between MT and SS were found in the modulation of the mu rhythm. All were characterized by event-related desynchronization (ERD) peaks at electrodes C3, C4, and CP3. Execution of the MT was associated with a significantly stronger ERD in the majority of sensorimotor electrodes [C3 (p < 0.01); CP3 (p < 0.05); C4 (p < 0.01)]. Classification accuracy of MT vs. REST was significantly higher than SS vs. REST (77% and 63%; p < 10-8). Classifiers trained on MT vs. REST were able to classify SS trials significantly above chance even though no motor commands were present during SS. Classifiers trained on SS performed better in classifying MT instead of SS. Conclusion: Our results challenge the notion that the modulation of the mu rhythm is a robust phenomenon for detecting efferent commands when afferent feedback is present. Instead, they indicate that the mu ERD caused by the processing of afferent feedback generates ERD patterns in the sensorimotor cortex that are masking the ERD patterns caused by the generation of efferent commands. Thus, processing of afferent feedback represents a considerable source of false positives when the mu rhythm is used for the detection of efferent commands.
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Affiliation(s)
- Maximilian Hommelsen
- Experimental Neurorehabilitation, Spinal Cord Injury Center, Heidelberg University Hospital, Heidelberg, Germany
| | - Matthias Schneiders
- Experimental Neurorehabilitation, Spinal Cord Injury Center, Heidelberg University Hospital, Heidelberg, Germany
| | - Christian Schuld
- Experimental Neurorehabilitation, Spinal Cord Injury Center, Heidelberg University Hospital, Heidelberg, Germany
| | - Philipp Keyl
- Experimental Neurorehabilitation, Spinal Cord Injury Center, Heidelberg University Hospital, Heidelberg, Germany
| | - Rüdiger Rupp
- Experimental Neurorehabilitation, Spinal Cord Injury Center, Heidelberg University Hospital, Heidelberg, Germany
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8
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Kirsch TD, Chanmugam A, Keyl P, Regan LA, Shahan J, Hexter DA, Kelen GD. Feasibility of an emergency department-based tuberculosis counseling and screening program. Acad Emerg Med 1999; 6:224-31. [PMID: 10192675 DOI: 10.1111/j.1553-2712.1999.tb00161.x] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
Abstract
OBJECTIVE To assess the feasibility and effectiveness of an ED-based tuberculosis (TB) screening program. METHODS A TB screening program of adult ED patients was conducted at a university hospital ED with 46,000 annual visits that serves a poor urban community. Patients were screened on weekdays during business hours. ED patients were counseled about the disease and the screening procedure and, after consent, purified protein derivative (PPD) tests were placed. Patients returned in 48-72 hours for reaction reading and post-test counseling. PPD-positive patients received a physical examination, chest x-ray, and HIV testing and were referred to a city TB clinic for possible treatment. RESULTS Overall, 873 patients were counseled, 630 were eligible for screening, and 374 (59.4%) consented to PPD testing. Of the 203 (54.1%) who returned, 32 (15.8%) were PPD-positive. No active case was detected, but 26 patients were referred to the health department. Eighteen kept their appointments and all 13 who were started on therapy completed treatment. Targeted screening of groups aged 55 years or more, nonwhite groups, and those with other high-risk factors would detect 84% of PPD-positive cases while testing only 48% of eligible patients. CONCLUSION An ED-based TB screening program is feasible and can identify many patients requiring treatment. Targeted screening of high-risk groups could reduce the program cost, but would miss some cases.
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Affiliation(s)
- T D Kirsch
- Department of Emergency Medicine, Michael Reese Hospital and Medical Center, Chicago, IL, USA
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9
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Verreault R, Stulginskas J, Keyl P, Read J, Pless IB. Use of automobile seat restraints by children in two Canadian cities. Can Med Assoc J 1982; 126:1163-8. [PMID: 7074437 PMCID: PMC1863383] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 01/23/2023]
Abstract
A telephone survey was conducted in Montreal and Calgary to determine the extent to which parents use safety restraints for their children in cars, the types of restraints used and the factors associated with such use. Of the combined sample 39% reported that their child regularly used a restraint, and 47% of the children in Montreal and 55% of those in Calgary wore an age-appropriate safety device. Birth order and the child's and the respondent's ages were significantly related to the use of restraints. However, the respondent's use of seatbelts was the factor most likely to affect the rate of use of restraints for children. Although a large proportion of the parents with younger children owned a carseat, one in four did not use it regularly. Parents who did not use carseats believed they were useless or dangerous; many preferred to hold the child in their laps or arms. The seatbelt law in Quebec and the existence of a carseat rental program in Calgary appear to have had some influence on the rate of the use of restraints for children.
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