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Prade, V.M. ; Sun, N. ; Shen, J. ; Feuchtinger, A. ; Kunzke, T. ; Buck, A. ; Schraml, P.* ; Moch, H.* ; Schwamborn, K.* ; Autenrieth, M.* ; Gschwend, J.E.* ; Erlmeier, F.* ; Hartmann, A.* ; Walch, A.K.

The synergism of spatial metabolomics and morphometry improves machine learning-based renal tumour subtype classification.

Clin. Transl. Med. 12:e666 (2022)
Publ. Version/Full Text Research data DOI PMC
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Publication type Article: Journal article
Document type Scientific Article
Corresponding Author
Keywords Machine Learning ; Mass Spectrometry Imaging ; Metabolomics ; Morphometry ; Renal Cell Carcinoma ; Tumour Of The Kidney ; Tumour Subtyping; Imaging Mass-spectrometry; Cell Carcinoma; Oncocytoma
ISSN (print) / ISBN 2001-1326
e-ISSN 2001-1326
Journal Clinical and translational medicine
Quellenangaben Volume: 12, Issue: 2, Pages: , Article Number: e666 Supplement: ,
Publisher Springer
Publishing Place The Atrium, Southern Gate, Chichester Po19 8sq, W Sussex, England
Non-patent literature Publications
Reviewing status Peer reviewed
Institute(s) Research Unit Analytical Pathology (AAP)
Grants Impulse and Networking Fund of the Helmholtz Association and the Helmholtz Zentrum München
Deutsche Forschungsgemeinschaft