학술논문
DNA methylation-based classification of sinonasal tumors
Document Type
article
Author
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; David Capper
Source
Nature Communications, Vol 13, Iss 1, Pp 1-14 (2022)
Subject
Language
English
ISSN
2041-1723
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.