Rj. Gerstle et al., The role of neural networks in improving the accuracy of MR spectroscopy for the diagnosis of head and neck squamous cell carcinoma, AM J NEUROR, 21(6), 2000, pp. 1133-1138
Citations number
27
Categorie Soggetti
Radiology ,Nuclear Medicine & Imaging","Neurosciences & Behavoir
BACKGROUND AND PURPOSE: MR Spectroscopy (MRS) has the unique ability to ana
lyze tissue at the molecular level noninvasively. The purpose of this study
was to determine if peak heights revealed by proton MRS (H-1-MRS) signals
showed that neural networks (NN) provided better accuracy than linear discr
iminant analysis (LDA) in differentiating head and neck squamous cell carci
noma (SCCA) from muscle
METHODS: In vitro 11-T H-1-MR spectra were obtained on SCCA tissue samples
(n = 16) and muscle (n = 12), The peak heights at seven metabolite resonanc
es were measured: olefinic acids at 5.3 ppm, inositol at 3.5 ppm, taurine a
t 3.4 ppm, choline (Cho) at 3.2 ppm, creatine (Cr) at 3.0 ppm, sialic acid
at 2.2 ppm, and methyl at 0.9 ppm. Using leave-one-out experimental design
and receiver operating characteristic curve analysis, the ability of NN and
LDA classifiers to distinguish SCCA from muscle were compared (given equal
weighting of false-negative and false-positive errors). These classifiers
were also compared with an existing method that forms a diagnosis by using
LDA of the Cho/Cr peak area ratio.
RESULTS: NN classifiers, which were identified using height data, achieved
better sensitivity and specificity rates in distinguishing SCAA from muscle
than did LDA using height or area data. Sensitivity/specificity for the NN
analysis of the seven metabolite peak heights were 87.5% and 83.3%, respec
tively, for a one-hidden-node network and 81.2% and 91.7%, respectively, fo
r a two-hidden-node network, Additional nodes did not improve accuracy, The
sensitivity and specificity were 81.2% and 50%, respectively, for LDA of t
he seven peak heights, and 68% and 83%, respectively, for LDA of the Cho/Cr
peak area ratio,
CONCLUSION: NN classifiers with peak height data were superior to LDA of th
e peak heights and LDA of the Cho/Cr peak area ratio for differentiating SC
CA from normal muscle, These results show neural network analysis cain impr
ove the diagnostic accuracy of H-1-MRS in differentiating muscle from malig
nant tissue. Further studies are necessary to confirm our initial findings.