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

Document type:: Letter; Research Support, Non-U.S. Gov't
Author(s):: Prade, Verena M; Sun, Na; Shen, Jian; Feuchtinger, Annette; Kunzke, Thomas; Buck, Achim; Schraml, Peter; Moch, Holger; Schwamborn, Kristina; Autenrieth, Michael; Gschwend, Jürgen E; Erlmeier, Franziska; Hartmann, Arndt; Walch, Axel
Title:: The synergism of spatial metabolomics and morphometry improves machine learning-based renal tumour subtype classification.
Journal title abbreviation:: Clin Transl Med
Year:: 2022
Journal volume:: 12
Journal issue:: 2
Fulltext / DOI:: doi:10.1002/ctm2.666
Pubmed ID:: http://view.ncbi.nlm.nih.gov/pubmed/35184396
TUM Institution:: Institut für Allgemeine Pathologie und Pathologische Anatomie (Dr. Mogler komm.); Klinik und Poliklinik für Urologie (Prof. Gschwend)
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