Quality control of 3D MRSI data in glioblastoma: Can we do without the experts?

Purpose Proton magnetic resonance spectroscopic imaging (1H MRSI) is a noninvasive technique for assessing tumor metabolism. Manual inspection is still the gold standard for quality control (QC) of spectra, but it is both time‐consuming and subjective. The aim of the present study was to assess auto...

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Bibliographic Details
Published in:Magnetic resonance in medicine Vol. 87; no. 4; pp. 1688 - 1699
Main Authors: Tensaouti, Fatima, Desmoulin, Franck, Gilhodes, Julia, Martin, Elodie, Ken, Soleakhena, Lotterie, Jean‐Albert, Noël, Georges, Truc, Gilles, Sunyach, Marie‐Pierre, Charissoux, Marie, Magné, Nicolas, Lubrano, Vincent, Péran, Patrice, Cohen‐Jonathan Moyal, Elizabeth, Laprie, Anne
Format: Journal Article
Language:English
Published: United States Wiley Subscription Services, Inc 01.04.2022
Wiley
Subjects:
Brain cancer
Brain Neoplasms - radiotherapy
Cancer
classification; glioblastoma
Data acquisition
Data analysis
Feature extraction
Glioblastoma
Glioblastoma - diagnostic imaging
Humans
Inspection
Life Sciences
Lipids
Magnetic resonance imaging
Magnetic Resonance Imaging - methods
Magnetic resonance spectroscopy
Magnetic Resonance Spectroscopy - methods
Proton magnetic resonance
Quality Control
random forest
Reproducibility of Results
Spectra
Spectrum analysis
Statistical analysis
Statistics
three‐dimensional magnetic resonance spectroscopic imaging (3D MRSI)
Tumors
classification; glioblastoma
random forest
quality control
three-dimensional magnetic resonance spectroscopic imaging (3D MRSI)
glioblastoma
classification
ISSN:0740-3194, 1522-2594, 1522-2594
Online Access:Get full text
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Summary:Purpose Proton magnetic resonance spectroscopic imaging (1H MRSI) is a noninvasive technique for assessing tumor metabolism. Manual inspection is still the gold standard for quality control (QC) of spectra, but it is both time‐consuming and subjective. The aim of the present study was to assess automatic QC of glioblastoma MRSI data using random forest analysis. Methods Data for 25 patients, acquired prospectively in a preradiotherapy examination, were submitted to postprocessing with syngo.MR Spectro (VB40A; Siemens) or Java‐based magnetic resonance user interface (jMRUI) software. A total of 28 features were extracted from each spectrum for the automatic QC. Three spectroscopists also performed manual inspections, labeling each spectrum as good or poor quality. All statistical analyses, with addressing unbalanced data, were conducted with R 3.6.1 (R Foundation for Statistical Computing; https://www.r‐project.org). Results The random forest method classified the spectra with an area under the curve of 95.5%, sensitivity of 95.8%, and specificity of 81.7%. The most important feature for the classification was Residuum_Lipids_Versus_Fit, obtained with syngo.MR Spectro. Conclusion The automatic QC method was able to distinguish between good‐ and poor‐quality spectra, and can be used by radiation oncologists who are not spectroscopy experts. This study revealed a novel set of MRSI signal features that are closely correlated with spectral quality.
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ISSN:0740-3194
1522-2594
1522-2594
DOI:10.1002/mrm.29098