Compatibility between 3T 1H SV-MRS data and automatic brain tumour diagnosis support systems based on databases of 1.5T 1H SV-MRS spectra.
OBJECTIVE
This study demonstrates that 3T SV-MRS data can be used with the currently available automatic brain tumour diagnostic classifiers which were trained on databases of 1.5T spectra. This will allow the existing large databases of 1.5T MRS data to be used for diagnostic classification of 3T spectra, and perhaps also the combination of 1.5T and 3T databases.


METHODS
Brain tumour classifiers trained with 154 1.5T spectra to discriminate among high grade malignant tumours and common grade II glial tumours were evaluated with a subsequently-acquired set of 155 1.5T and 37 3T spectra. A similarity study between spectra and main brain tumour metabolite ratios for both field strengths (1.5T and 3T) was also performed.


RESULTS
Our results showed that classifiers trained with 1.5T samples had similar accuracy for both test datasets (0.87 ± 0.03 for 1.5T and 0.88 ± 0.03 for 3.0T). Moreover, non-significant differences were observed with most metabolite ratios and spectral patterns.


CONCLUSIONS
These results encourage the use of existing classifiers based on 1.5T datasets for diagnosis with 3T (1)H SV-MRS. The large 1.5T databases compiled throughout many years and the prediction models based on 1.5T acquisitions can therefore continue to be used with data from the new 3T instruments.
Universitat Internacional Valenciana, Valencia, Spain, elfusgar@upv.es
Elies
E
Fuster-Garcia
Clara
C
Navarro
Javier
J
Vicente
Salvador
S
Tortajada
Juan M
JM
García-Gómez
Carlos
C
Sáez
Jorge
J
Calvar
John
J
Griffiths
Margarida
M
Julià-Sapé
Franklyn A
FA
Howe
Jesús
J
Pujol
Andrew C
AC
Peet
Arend
A
Heerschap
Angel
A
Moreno-Torres
M C
MC
Martínez-Bisbal
Beatriz
B
Martínez-Granados
Pieter
P
Wesseling
Wolfhard
W
Semmler
Jaume
J
Capellades
Carles
C
Majós
Angel
A
Alberich-Bayarri
Antoni
A
Capdevila
Daniel
D
Monleón
Luis
L
Martí-Bonmatí
Carles
C
Arús
Bernardo
B
Celda
Montserrat
M
Robles
D001932:Brain Neoplasms / Q000175:diagnosis* / Q000378:metabolism; D016208:Databases, Factual; D006801:Humans; D009682:Magnetic Resonance Spectroscopy / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D011522:Protons; D012680:Sensitivity and Specificity
D011522:Protons
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