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Verfasst von:Kriegsmann, Mark [VerfasserIn]   i
 Casadonte, Rita [VerfasserIn]   i
 Maurer, Nadine [VerfasserIn]   i
 Stoehr, Christine [VerfasserIn]   i
 Erlmeier, Franziska [VerfasserIn]   i
 Moch, Holger [VerfasserIn]   i
 Junker, Kerstin [VerfasserIn]   i
 Zgorzelski, Christiane [VerfasserIn]   i
 Weichert, Wilko [VerfasserIn]   i
 Schwamborn, Kristina [VerfasserIn]   i
 Deininger, Sören-Oliver [VerfasserIn]   i
 Gaida, Matthias [VerfasserIn]   i
 Mechtersheimer, Gunhild [VerfasserIn]   i
 Stenzinger, Albrecht [VerfasserIn]   i
 Schirmacher, Peter [VerfasserIn]   i
 Hartmann, Arndt [VerfasserIn]   i
 Kriegsmann, Joerg [VerfasserIn]   i
 Kriegsmann, Katharina [VerfasserIn]   i
Titel:Mass spectrometry imaging differentiates chromophobe renal cell carcinoma and renal oncocytoma with high accuracy
Verf.angabe:Mark Kriegsmann, Rita Casadonte, Nadine Maurer, Christine Stoehr, Franziska Erlmeier, Holger Moch, Kerstin Junker, Christiane Zgorzelski, Wilko Weichert, Kristina Schwamborn, Sören-Oliver Deininger, Matthias Gaida, Gunhild Mechtersheimer, Albrecht Stenzinger, Peter Schirmacher, Arndt Hartmann, Joerg Kriegsmann, Katharina Kriegsmann
E-Jahr:2020
Jahr:2020.08.21
Umfang:9 S.
Fussnoten:Gesehen am 26.10.2020
Titel Quelle:Enthalten in: Journal of cancer
Ort Quelle:Wyoming, NSW : Ivyspring Internat. Publ., 2010
Jahr Quelle:2020
Band/Heft Quelle:11(2020), 20, Seite 6081-6089
ISSN Quelle:1837-9664
Abstract:Background: While subtyping of the majority of malignant chromophobe renal cell carcinoma (cRCC) and benign renal oncocytoma (rO) is possible on morphology alone, additional histochemical, immunohistochemical or molecular investigations are required in a subset of cases. As currently used histochemical and immunohistological stains as well as genetic aberrations show considerable overlap in both tumors, additional techniques are required for differential diagnostics. Mass spectrometry imaging (MSI) combining the detection of multiple peptides with information about their localization in tissue may be a suitable technology to overcome this diagnostic challenge. Patients and methods: Formalin-fixed paraffin embedded (FFPE) tissue specimens from cRCC (n=71) and rO (n=64) were analyzed by MSI. Data were classified by linear discriminant analysis (LDA), classification and regression trees (CART), k-nearest neighbors (KNN), support vector machine (SVM), and random forest (RF) algorithm with internal cross validation and visualized by t-distributed stochastic neighbor embedding (t-SNE). Most important variables for classification were identified and the classification algorithm was optimized. Results: Applying different machine learning algorithms on all m/z peaks, classification accuracy between cRCC and rO was 85%, 82%, 84%, 77% and 64% for RF, SVM, KNN, CART and LDA. Under the assumption that a reduction of m/z peaks would lead to improved classification accuracy, m/z peaks were ranked based on their variable importance. Reduction to six most important m/z peaks resulted in improved accuracy of 89%, 85%, 85% and 85% for RF, SVM, KNN, and LDA and remained at the level of 77% for CART. t-SNE showed clear separation of cRCC and rO after algorithm improvement. Conclusion: In summary, we acquired MSI data on FFPE tissue specimens of cRCC and rO, performed classification and detected most relevant biomarkers for the differential diagnosis of both diseases. MSI data might be a useful adjunct method in the differential diagnosis of cRCC and rO.
DOI:doi:10.7150/jca.47698
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/10.7150/jca.47698
 Volltext: https://www.jcancer.org/v11p6081.htm
 DOI: https://doi.org/10.7150/jca.47698
Datenträger:Online-Ressource
Sprache:eng
K10plus-PPN:1736566547
Verknüpfungen:→ Zeitschrift

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