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

Abstract

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 is to examine the potential of matrix-assisted laser desorption/ionization (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 are analyzed using MALDI-Imaging. Classifications are 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 result 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 are 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 may support the development of new prognostic parameters in the assessment of EOC.

Keywords: histotype classification; imaging mass spectrometry; machine learning; ovarian cancer.