MALDI-Imaging for Classification of Epithelial Ovarian Cancer Histotypes from a Tissue Microarray using Machine Learning Methods.

PURPOSE: Precise histological classification of epithelial ovarian cancer (EOC) has immanent diagnostic and therapeutic consequences, but remains challenging in histological routine. The aim of this pilot study was to examine the potential of MALDI-Imaging mass spectrometry in combination with machine learning methods to classify EOC histological subtypes from tissue microarray.

EXPERIMENTAL DESIGN: Formalin-fixed-paraffin-embedded tissue of 20 patients with ovarian clear-cell, 14 low-grade serous, 19 high-grade serous ovarian carcinomas and 14 serous borderline tumors were analysed using MALDI-Imaging. Classifications were computed by linear discriminant analysis (LDA), support vector machines with linear (SVM-lin) and radial basis function kernels (SVM-rbf), a neural network (NN), and a convolutional neural network (CNN).

RESULTS: MALDI-Imaging and machine learning methods results in classification of EOC histotypes with mean accuracy of 80% for LDA, 80% SVM-lin, 74% SVM-rbf, 83% NN and 85% CNN. Based on sensitivity (69%-100%) and specificity (90-99%), CCN and NN were most suited to EOC classification.

CONCLUSION AND CLINICAL RELEVANCE: The pilot study demonstrates the potential of MALDI-Imaging derived proteomic classifiers in combination with machine learning algorithms to discriminate EOC histotypes. Applications might support the development of new prognostic parameters in the assessment of EOC. This article is protected by copyright. All rights reserved.