@article{154b603ed8844efba4273687fcaacc6c,
title = "Metabolomics, machine learning and immunohistochemistry to predict succinate dehydrogenase mutational status in phaeochromocytomas and paragangliomas",
keywords = "Krebs cycle metabolites, LC–MS/MS, diagnostics, linear discriminant analysis, mass spectrometry, metabolite profiling, multi-observer, prediction models, succinate to fumarate ratio, variants of unknown significance",
author = "Paal Wallace and Catleen Conrad and Sascha Bruckmann and Ying Pang and Eduardo Caleiras and Masanori Murakami and Esther Korpershoek and Zhenping Zhuang and Elena Rapizzi and Matthias Kroiss and Volker Gudziol and Timmers, \{Henri J L M\} and Massimo Mannelli and Jens Pietzsch and Felix Beuschlein and Karel Pacak and Mercedes Robledo and Barbara Klink and Mirko Peitzsch and Gill, \{Anthony J\} and Tischler, \{Arthur S\} and \{De Krijger\}, Ronald and Thomas Papathomas and Daniela Aust and Graeme Eisenhofer and Susan Richter",
year = "2020",
month = may,
day = "27",
doi = "10.1002/path.5472",
language = "English",
volume = "251",
pages = "378--387",
journal = "Journal of Pathology",
issn = "0022-3417",
publisher = "Wiley",
number = "4",
}