1. DNA methylation-based classification of sinonasal tumors
- Author
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Philipp Jurmeister, Stefanie Glöß, Renée Roller, Maximilian Leitheiser, Simone Schmid, Liliana H. Mochmann, Emma Payá Capilla, Rebecca Fritz, Carsten Dittmayer, Corinna Friedrich, Anne Thieme, Philipp Keyl, Armin Jarosch, Simon Schallenberg, Hendrik Bläker, Inga Hoffmann, Claudia Vollbrecht, Annika Lehmann, Michael Hummel, Daniel Heim, Mohamed Haji, Patrick Harter, Benjamin Englert, Stephan Frank, Jürgen Hench, Werner Paulus, Martin Hasselblatt, Wolfgang Hartmann, Hildegard Dohmen, Ursula Keber, Paul Jank, Carsten Denkert, Christine Stadelmann, Felix Bremmer, Annika Richter, Annika Wefers, Julika Ribbat-Idel, Sven Perner, Christian Idel, Lorenzo Chiariotti, Rosa Della Monica, Alfredo Marinelli, Ulrich Schüller, Michael Bockmayr, Jacklyn Liu, Valerie J. Lund, Martin Forster, Matt Lechner, Sara L. Lorenzo-Guerra, Mario Hermsen, Pascal D. Johann, Abbas Agaimy, Philipp Seegerer, Arend Koch, Frank Heppner, Stefan M. Pfister, David T. W. Jones, Martin Sill, Andreas von Deimling, Matija Snuderl, Klaus-Robert Müller, Erna Forgó, Brooke E. Howitt, Philipp Mertins, Frederick Klauschen, and David Capper
- Subjects
Science - Abstract
Sinonasal tumour diagnosis can be complicated by the heterogeneity of disease and classification systems. Here, the authors use machine learning to classify sinonasal undifferentiated carcinomas into 4 molecular classe with differences in differentiation state and clinical outcome.
- Published
- 2022
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