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

Kyathanahally, Sreenath Pruthviraj; Mocioiu, Victor; da Silva Mendes Pedrosa de Barros, Nuno Miguel; Slotboom, Johannes; Wright, Alan J; Julià-Sapé, Margarida; Arús, Carles; Kreis, Roland (2018). Quality of clinical brain tumor MR spectra judged by humans and machine learning tools. Magnetic resonance in medicine, 79(5), pp. 2500-2510. Wiley-Liss 10.1002/mrm.26948

[img] Text
Kyathanahally_et_al-2017-Magnetic_Resonance_in_Medicine.pdf - Published Version
Restricted to registered users only
Available under License Publisher holds Copyright.
read-only full text at http://rdcu.be/Ht8p

Download (448kB) | Request a copy

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. Magn Reson Med, 2017. © 2017 International Society for Magnetic Resonance in Medicine.

Item Type:

Journal Article (Original Article)

Division/Institute:

04 Faculty of Medicine > Department of Radiology, Neuroradiology and Nuclear Medicine (DRNN) > Institute of Diagnostic, Interventional and Paediatric Radiology > DCR Magnetic Resonance Spectroscopy and Methodology (AMSM)
04 Faculty of Medicine > Department of Radiology, Neuroradiology and Nuclear Medicine (DRNN) > Institute of Diagnostic and Interventional Neuroradiology
04 Faculty of Medicine > Pre-clinic Human Medicine > BioMedical Research (DBMR) > Forschungsbereich Pavillon 52 > Abt. Magnetresonanz-Spektroskopie und Methodologie, AMSM

Graduate School:

Graduate School for Cellular and Biomedical Sciences (GCB)

UniBE Contributor:

Kyathanahally, Sreenath Pruthviraj; da Silva Mendes Pedrosa de Barros, Nuno Miguel; Slotboom, Johannes and Kreis, Roland

Subjects:

600 Technology > 610 Medicine & health

ISSN:

0740-3194

Publisher:

Wiley-Liss

Funders:

[42] Schweizerischer Nationalfonds
[UNSPECIFIED] European Marie-Curie Initial Training Network. PITN-GA-2012-316679, 2013-2017
[UNSPECIFIED] Ministerio de Economía y Competitividad (MINECO) of Spain . Grant Number: MOLIMAGLIO (SAF2014-52332-R

Language:

English

Submitter:

Martin Zbinden

Date Deposited:

15 Nov 2017 08:38

Last Modified:

06 Mar 2018 10:56

Publisher DOI:

10.1002/mrm.26948

Related URLs:

PubMed ID:

28994492

Uncontrolled Keywords:

brain tumors classification inter-rater variability machine learning magnetic resonance spectroscopy quality assessment quality control

BORIS DOI:

10.7892/boris.106179

URI:

https://boris.unibe.ch/id/eprint/106179

Actions (login required)

Edit item Edit item
Provide Feedback