Front Cover: MALDI‐Imaging for Classification of Epithelial Ovarian Cancer Histotypes from a Tissue Microarray Using Machine Learning Methods

Abstract

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 https://doi.org/10.1002/prca.201700181.

Matrix-assisted laser desorption ionisation (MALDI) mass spectrometry (MS): basics and clinical applications

“…This augments the possibility to identify candidates for disease biomarkers. The most common condition where the method has been applied is cancer such as breast cancer [112], ovarian cancer [113], brain cancer [114] and prostate cancer [44]. Recently, in epithelial ovarian cancer (EOC), Klein et al [113] showed the advantages of this technique for the classification of histotypes from a tissue microarray to enable the development of new prognostic parameters in the tissue assessment of EOC, to enhance personalised treatment.…”

Surface Imaging in Applied Research

“…In recent years, a special place in mass spectrometry has been occupied by ways of studying surfaces or compounds on them. One of the latest developments in this area is mass spectrometric imaging, which is a way of obtaining distribution diagrams of a particular compound over different model surfaces [8][9][10]. At the same time, imaging is rarely used to study the surfaces themselves [11,12].…”