American Journal of Neuroradiology
DOI 10.3174/ajnr.A1106
PEDIATRICS
Classification of Childhood White Matter Disorders Using Proton MR Spectroscopic Imaging
From the Departments of Neuroradiology (A.B., G.C., U.D., A.E., L.F.) and Child Neurology (M.B., I.M., G.U.), Fondazione IRCCS Istituto Neurologico "Carlo Besta," Milan, Italy; Department of Radiology (E.H.H.), University of Pennsylvania, Philadelphia, Pa; and Russell H Morgan Department of Radiology and Radiological Science (P.B.B.), Johns Hopkins University, Baltimore, Md.
Please address correspondence to Alberto Bizzi, MD, Dept of Neuroradiology, Fondazione IRCCS Istituto Neurologico "Carlo Besta," Via Celoria, 11, 20133 Milan, Italy; e-mail: alberto_bizzi{at}fastwebnet.it
BACKGROUND AND PURPOSE: Childhood white matter disorders often show similar MR imaging signal-intensity changes, despite different underlying pathophysiologies. The purpose of this study was to determine if proton MR spectroscopic imaging (1H-MRSI) may help identify tissue pathophysiology in patients with leukoencephalopathies.
MATERIALS AND METHODS: Seventy patients (mean age, 6; range, 0.66–17 years) were prospectively examined by 1H-MRSI; a diagnosis of leukoencephalopathy due to known genetic defects leading to lack of formation, breakdown of myelin, or loss of white matter tissue attenuation (rarefaction) was made in 47 patients. The diagnosis remained undefined (UL) in 23 patients. Patients with definite diagnoses were assigned (on the basis of known pathophysiology) to 3 groups corresponding to hypomyelination, white matter rarefaction, and demyelination. Choline (Cho), creatine (Cr), and N-acetylaspartate (NAA) signals from 6 white matter regions and their intra- and intervoxel (relative to gray matter) ratios were measured. Analysis of variance was performed by diagnosis and by pathophysiology group. Stepwise linear discriminant analysis was performed to construct a model to predict pathophysiology on the basis of 1H-MRSI, and was applied to the UL group.
RESULTS: Analysis of variance by diagnosis showed 3 main metabolic patterns. Analysis of variance by pathophysiology showed significant differences for Cho/NAA (P < .001), Cho/Cr (P < .004), and NAA/Cr (P < .002). Accuracy of the linear discriminant analysis model was 75%, with Cho/Cr and NAA/Cr being the best parameters for classification. On the basis of the linear discriminant analysis model, 61% of the subjects in the UL group were classified as hypomyelinating.
CONCLUSION: 1H-MRSI provides information on tissue pathophysiology and may, therefore, be a valuable tool in the evaluation of patients with leukoencephalopathies.