TY - JOUR
T1 - Compatibility between 3T 1H SV-MRS data and automatic brain tumour diagnosis support systems based on databases of 1.5T 1H SV-MRS spectra
AU - Fuster-Garcia, Elies
AU - Navarro, Clara
AU - Vicente, Javier
AU - Tortajada, Salvador
AU - García-Gómez, Juan M.
AU - Sáez, Carlos
AU - Calvar, Jorge
AU - Griffiths, John
AU - Julià-Sapé, Margarida
AU - Howe, Franklyn A.
AU - Pujol, Jesús
AU - Peet, Andrew C.
AU - Heerschap, Arend
AU - Moreno-Torres, Àngel
AU - Martínez-Bisbal, M. C.
AU - Martínez-Granados, Beatriz
AU - Wesseling, Pieter
AU - Semmler, Wolfhard
AU - Capellades, Jaume
AU - Majós, Carles
AU - Alberich-Bayarri, Àngel
AU - Capdevila, Antoni
AU - Monleón, Daniel
AU - Martí-Bonmatí, Luis
AU - Arús, Carles
AU - Celda, Bernardo
AU - Robles, Montserrat
PY - 2011/2
Y1 - 2011/2
N2 - Object: 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. Materials and 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. Conclusion: These results encourage the use of existing classifiers based on 1.5T datasets for diagnosis with 3T 1H 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.
AB - Object: 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. Materials and 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. Conclusion: These results encourage the use of existing classifiers based on 1.5T datasets for diagnosis with 3T 1H 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.
KW - Brain tumours
KW - Clinical decision support systems
KW - Magnetic resonance spectroscopy
UR - http://www.scopus.com/inward/record.url?scp=79951809860&partnerID=8YFLogxK
U2 - 10.1007/s10334-010-0241-8
DO - 10.1007/s10334-010-0241-8
M3 - Article
C2 - 21249420
AN - SCOPUS:79951809860
SN - 0968-5243
VL - 24
SP - 35
EP - 42
JO - Magnetic Resonance Materials in Physics, Biology and Medicine
JF - Magnetic Resonance Materials in Physics, Biology and Medicine
IS - 1
ER -