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Quality of clinical brain tumor MR spectra judged by humans and machine learning tools

Type of publication Peer-reviewed
Publikationsform Original article (peer-reviewed)
Author Kyathanahally, S. P.; Mocioiu, V.; Pedrosa de Barros, N.; Slotboom, J.; Wright, A. J.; Julià-Sapé, M.; Arús, C.; Kreis, R.
Project Magnetic resonance techniques to determine metabolite levels: extending scope and clinical robustness
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Original article (peer-reviewed)

Journal Magn Reson Med
Title of proceedings Magn Reson Med
DOI 10.1002/mrm.26948

Open Access

URL http://rdcu.be/wEm6
Type of Open Access Publisher (Gold Open Access)

Abstract

PURPOSE: To investigate and compare human judgment and machine learning tools for quality assessment of clinical MR spectra of brain tumors. METHODS: A very large set of 2574 single voxel spectra with short and long echo time from the eTUMOUR and INTERPRET databases were used for this analysis. Original human quality ratings from these studies as well as new human guidelines were used to train different machine learning algorithms for automatic quality control (AQC) based on various feature extraction methods and classification tools. The performance was compared with variance in human judgment. RESULTS: AQC built using the RUSBoost classifier that combats imbalanced training data performed best. When furnished with a large range of spectral and derived features where the most crucial ones had been selected by the TreeBagger algorithm it showed better specificity (98%) in judging spectra from an independent test-set than previously published methods. Optimal performance was reached with a virtual three-class ranking system. CONCLUSION: Our results suggest that feature space should be relatively large for the case of MR tumor spectra and that three-class labels may be beneficial for AQC. The best AQC algorithm showed a performance in rejecting spectra that was comparable to that of a panel of human expert spectroscopists.
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