von David Capper ; David T. W. Jones ; Daniel Schrimpf ; Dominik Sturm ; Christian Kölsche ; Felix Sahm ; David Reuss ; Annekathrin Kratz ; Annika K. Wefers ; Kristin Huang ; Kristian Wilfried Pajtler ; Leonille Schweizer ; Damian Stichel ; Florian Selt ; Hendrik Witt ; Till Milde ; Olaf Witt ; Wolfram Scheurlen ; Christoph Geisenberger ; Stefanie Brehmer ; Marcel Seiz-Rosenhagen ; Daniel Hänggi ; Andreas Kulozik ; Axel Benner ; Martin Bendszus ; Jürgen Debus ; Michael Platten ; Andreas Unterberg ; Wolfgang Wick ; Marcel Kool ; Christel Herold-Mende ; Andreas von Deimling ; Stefan Pfister ; Hermann L. Müller
Accurate pathological diagnosis is crucial for optimal management of patients with cancer. For the approximately 100 known tumour types of the central nervous system, standardization of the diagnostic process has been shown to be particularly challenging—with substantial inter-observer variability in the histopathological diagnosis of many tumour types. Here we present a comprehensive approach for the DNA methylation-based classification of central nervous system tumours across all entities and age groups, and demonstrate its application in a routine diagnostic setting. We show that the availability of this method may have a substantial impact on diagnostic precision compared to standard methods, resulting in a change of diagnosis in up to 12% of prospective cases. For broader accessibility, we have designed a free online classifier tool, the use of which does not require any additional onsite data processing. Our results provide a blueprint for the generation of machine-learning-based tumour classifiers across other cancer entities, with the potential to fundamentally transform tumour pathology.
Nature London [u.a.] : Nature Publ. Group, 1869 555(2018), 7697, Seite 469-474 Online-Ressource
von Stefanie Brück ; Christopher Krause ; R. Turrisi ; L. Beverina ; Sebastian Wilken ; Wolfgang Saak ; Arne Lützen ; Holger Borchert ; Manuela Schiek ; Jürgen Parisi
