MALDI-Imaging for Classification of Epithelial Ovarian Cancer Histotypes from a Tissue Microarray Using Machine Learning Methods.
Proteomics Clin Appl
; 13(1): e1700181, 2019 01.
Article
in En
| MEDLINE
| ID: mdl-30471200
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. EXPERIMENTALDESIGN:
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.Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Ovarian Neoplasms
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Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization
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Tissue Array Analysis
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Molecular Imaging
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Machine Learning
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Carcinoma, Ovarian Epithelial
Limits:
Female
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Humans
/
Middle aged
Language:
En
Journal:
Proteomics Clin Appl
Journal subject:
BIOQUIMICA
Year:
2019
Document type:
Article
Affiliation country:
Germany