| |  Online-Ressource |
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| Verfasst von: | Kriegsmann, Mark [VerfasserIn]  |
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| | Zgorzelski, Christiane [VerfasserIn] |
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| | Casadonte, Rita [VerfasserIn] |
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| | Schwamborn, Kristina [VerfasserIn] |
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| | Muley, Thomas [VerfasserIn] |
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| | Winter, Hauke [VerfasserIn] |
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| | Eichhorn, Martin E. [VerfasserIn] |
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| | Eichhorn, Florian [VerfasserIn] |
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| | Warth, Arne [VerfasserIn] |
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| | Deininger, Soeren-Oliver [VerfasserIn] |
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| | Christopoulos, Petros [VerfasserIn] |
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| | Thomas, Michael [VerfasserIn] |
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| | Longerich, Thomas [VerfasserIn] |
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| | Stenzinger, Albrecht [VerfasserIn] |
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| | Weichert, Wilko [VerfasserIn] |
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| | Müller-Tidow, Carsten [VerfasserIn] |
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| | Kriegsmann, Jörg [VerfasserIn] |
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| | Schirmacher, Peter [VerfasserIn] |
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| | Kriegsmann, Katharina [VerfasserIn] |
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| Titel: | Mass spectrometry imaging for reliable and fast classification of non-small cell lung cancer subtypes |
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| Verf.angabe: | Mark Kriegsmann, Christiane Zgorzelski, Rita Casadonte, Kristina Schwamborn, Thomas Muley, Hauke Winter, Martin Eichhorn, Florian Eichhorn, Arne Warth, Soeren-Oliver Deininger, Petros Christopoulos, Michael Thomas, Thomas Longerich, Albrecht Stenzinger, Wilko Weichert, Carsten Müller-Tidow, Jörg Kriegsmann, Peter Schirmacher and Katharina Kriegsmann |
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| E-Jahr: | 2020 |
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| Jahr: | 21 September 2020 |
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| Umfang: | 14 S. |
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| Fussnoten: | Gesehen am 04.12.2020 |
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| Titel Quelle: | Enthalten in: Cancers |
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| Ort Quelle: | Basel : MDPI, 2009 |
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| Jahr Quelle: | 2020 |
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| Band/Heft Quelle: | 12(2020,9) Artikel-Nummer 2704, 14 Seiten |
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| ISSN Quelle: | 2072-6694 |
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| Abstract: | Subtyping of non-small cell lung cancer (NSCLC) is paramount for therapy stratification. In this study, we analyzed the largest NSCLC cohort by mass spectrometry imaging (MSI) to date. We sought to test different classification algorithms and to validate results obtained in smaller patient cohorts. Tissue microarrays (TMAs) from including adenocarcinoma (ADC, n = 499) and squamous cell carcinoma (SqCC, n = 440), were analyzed. Linear discriminant analysis, support vector machine, and random forest (RF) were applied using samples randomly assigned for training (66%) and validation (33%). The m/z species most relevant for the classification were identified by on-tissue tandem mass spectrometry and validated by immunohistochemistry (IHC). Measurements from multiple TMAs were comparable using standardized protocols. RF yielded the best classification results. The classification accuracy decreased after including less than six of the most relevant m/z species. The sensitivity and specificity of MSI in the validation cohort were 92.9% and 89.3%, comparable to IHC. The most important protein for the discrimination of both tumors was cytokeratin 5. We investigated the largest NSCLC cohort by MSI to date and found that the classification of NSCLC into ADC and SqCC is possible with high accuracy using a limited set of m/z species. |
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| DOI: | doi:10.3390/cancers12092704 |
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| URL: | Bitte beachten Sie: Dies ist ein Bibliographieeintrag. Ein Volltextzugriff für Mitglieder der Universität besteht hier nur, falls für die entsprechende Zeitschrift/den entsprechenden Sammelband ein Abonnement besteht oder es sich um einen OpenAccess-Titel handelt.
Volltext ; Verlag: https://doi.org/10.3390/cancers12092704 |
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| | Volltext: https://www.mdpi.com/2072-6694/12/9/2704 |
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| | DOI: https://doi.org/10.3390/cancers12092704 |
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| Datenträger: | Online-Ressource |
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| Sprache: | eng |
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| Sach-SW: | lung cancer |
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| | mass spectrometry |
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| | mass spectrometry imaging |
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| | NSCLC |
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| K10plus-PPN: | 1742035906 |
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| Verknüpfungen: | → Zeitschrift |
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Mass spectrometry imaging for reliable and fast classification of non-small cell lung cancer subtypes / Kriegsmann, Mark [VerfasserIn]; 21 September 2020 (Online-Ressource)
