A comparison of non‐negative matrix underapproximation methods for the decomposition of magnetic resonance spectroscopy data from human brain tumors

Magnetic resonance spectroscopy (MRS) is an MR technique that provides information about the biochemistry of tissues in a noninvasive way. MRS has been widely used for the study of brain tumors, both preoperatively and during follow‐up. In this study, we investigated the performance of a range of va...

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Bibliographic Details
Published in:NMR in biomedicine Vol. 36; no. 12; p. e5020
Main Authors: Ungan, Gulnur, Arús, Carles, Vellido, Alfredo, Julià‐Sapé, Margarida
Format: Journal Article
Language:English
Published: England Wiley Subscription Services, Inc 01.12.2023
Subjects:
Algorithms
Biological products
Brain
Brain cancer
Brain Neoplasms - pathology
Brain tumors
Classification
Diagnostic systems
Factorization
Feature extraction
Humans
Magnetic resonance spectroscopy
Magnetic Resonance Spectroscopy - methods
Meningeal Neoplasms
Meningioma
Spectroscopy
Spectrum analysis
Tumors
meningioma
glioblastoma
metastasis
brain tumor
magnetic resonance spectroscopy
non-negative matrix factorization
non-negative matrix underapproximation
low grade glioma
ISSN:0952-3480, 1099-1492, 1099-1492
Online Access:Get full text
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Summary:Magnetic resonance spectroscopy (MRS) is an MR technique that provides information about the biochemistry of tissues in a noninvasive way. MRS has been widely used for the study of brain tumors, both preoperatively and during follow‐up. In this study, we investigated the performance of a range of variants of unsupervised matrix factorization methods of the non‐negative matrix underapproximation (NMU) family, namely, sparse NMU, global NMU, and recursive NMU, and compared them with convex non‐negative matrix factorization (C‐NMF), which has previously shown a good performance on brain tumor diagnostic support problems using MRS data. The purpose of the investigation was 2‐fold: first, to ascertain the differences among the sources extracted by these methods; and second, to compare the influence of each method in the diagnostic accuracy of the classification of brain tumors, using them as feature extractors. We discovered that, first, NMU variants found meaningful sources in terms of biological interpretability, but representing parts of the spectrum, in contrast to C‐NMF; and second, that NMU methods achieved better classification accuracy than C‐NMF for the classification tasks when one class was not meningioma.
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ISSN:0952-3480
1099-1492
1099-1492
DOI:10.1002/nbm.5020