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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
Open Access Gold
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Publication type Article: Journal article
Document type Scientific Article
Keywords Machine Learning ; Mass Spectrometry Imaging ; Metabolomics ; Morphometry ; Renal Cell Carcinoma ; Tumour Of The Kidney ; Tumour Subtyping; Imaging Mass-spectrometry; Cell Carcinoma; Oncocytoma
Language english
Publication Year 2022
HGF-reported in Year 2022
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
Reviewing status Peer reviewed
Institute(s) Research Unit Analytical Pathology (AAP)
CF Pathology & Tissue Analytics (CF-PTA)
POF-Topic(s) 30205 - Bioengineering and Digital Health
30505 - New Technologies for Biomedical Discoveries
Research field(s) Enabling and Novel Technologies
PSP Element(s) G-500390-001
A-630600-001
Grants Impulse and Networking Fund of the Helmholtz Association and the Helmholtz Zentrum München
Deutsche Forschungsgemeinschaft
DOI 10.1002/ctm2.666
WOS ID WOS:000761320600001
PubMed ID 35184396
Erfassungsdatum 2022-05-04
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