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Front Cover: MALDI‐Imaging for Classification of Epithelial Ovarian Cancer Histotypes from a Tissue Microarray Using Machine Learning Methods
Author(s) -
Klein Oliver,
Kanter Frederic,
Kulbe Hagen,
Jank Paul,
Denkert Carsten,
Nebrich Grit,
Schmitt Wolfgang D.,
Wu Zhiyang,
Kunze Catarina A.,
Sehouli Jalid,
DarbEsfahani Silvia,
Braicu Ioana,
Lellmann Jan,
Thiele Herbert,
Taube Eliane T.
Publication year - 2019
Publication title -
proteomics – clinical applications
Language(s) - English
Resource type - Reports
SCImago Journal Rank - 0.948
H-Index - 54
eISSN - 1862-8354
pISSN - 1862-8346
DOI - 10.1002/prca.201970011
Subject(s) - front cover , tissue microarray , epithelial ovarian cancer , ovarian cancer , cancer , cover (algebra) , medicine , pathology , oncology , artificial intelligence , cancer research , computer science , mechanical engineering , engineering
Precise histological classification of epithelial ovarian cancer (EOC) subtypes is important for personalized treatment. The image, designed by Maria Hänel, illustrates the potential of imaging mass spectrometry combined with machine learning methods to explore spatial proteomic levels, enabling the classification of EOC histological subtypes. Applications include the development of new prognostic parameters in the tissue assessment of EOC. This is reported by Oliver Klein, Frederic Kanter, Hagen Kulbe, Paul Jank, Carsten Denkert, Grit Nebrich, Wolfgang D. Schmitt, Zhiyang Wu, Catarina A. Kunze, Jalid Sehouli, Silvia Darb‐Esfahani, Ioana Braicu, Jan Lellmann, Herbert Thiele, Eliane T. Taube in article 1700181 .