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  • Title: Clinical application of proton magnetic resonance spectroscopy in the diagnosis of intracranial mass lesions.
    Author: Möller-Hartmann W, Herminghaus S, Krings T, Marquardt G, Lanfermann H, Pilatus U, Zanella FE.
    Journal: Neuroradiology; 2002 May; 44(5):371-81. PubMed ID: 12012120.
    Abstract:
    Diagnosis of primary and secondary brain tumours and other focal intracranial mass lesions based on imaging procedures alone is still a challenging problem. Proton magnetic resonance spectroscopy (1H-MRS) gives completely different information related to cell membrane proliferation, neuronal damage, energy metabolism and necrotic transformation of brain or tumour tissues. Our purpose was to evaluate the clinical utility of 1H-MRS added to MRI for the differentiation of intracranial neoplastic and non-neoplastic mass lesions. 176 mostly histologically verified lesions were studied with a constant clinically available single volume 1H-MRS protocol following routine MRI. 12 spectra (6.8%) were not of satisfactory diagnostic quality; 164 spectroscopic data sets were therefore available for definitive evaluation. Our study shows that spectroscopy added to MRI helps in tissue characterization of intracranial mass lesions, thereby leading to an improved diagnosis of focal brain disease. Non-neoplastic lesions such as cerebral infarctions and brain abscesses are marked by decreases in choline (Cho), creatine (Cr) and N-acetyl-aspartate (NAA), while tumours generally have elevated Cho and decreased levels of Cr and NAA. Gliomas exhibit significantly increased Cho and lipid formation with higher WHO tumour grading. Metastases have elevated Cho similar to anaplastic astrocytomas, but can be differentiated from high-grade gliomas by their higher lipid levels. Extra-axial tumours, i.e. meningiomas and neurinomas, are characterized by a nearly complete absence of the neuronal marker NAA. The additive information of 1H-MRS led to a 15.4%-higher number of correct diagnoses, to 6.2% fewer incorrect and 16% fewer equivocal diagnoses than with structural MRI data alone.
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