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Anal Chem. 2010 Apr 1;82(7):2621-5. doi: 10.1021/ac100113w.

Imaging of meningioma progression by matrix-assisted laser desorption ionization time-of-flight mass spectrometry.

Author information

1
Surgical Molecular Imaging Laboratory, Department of Neurosurgery, 221, Longwood Avenue, BLI-137, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts 02115, USA. nagar@bwh.harvard.edu

Abstract

Often considered benign, meningiomas represent 32% of intracranial tumors with three grades of malignancy defined by the World Health Organization (WHO) histology based classification. Malignant meningiomas are associated with less than 2 years median survival. The inability to predict recurrence and progression of meningiomas induces significant anxiety for patients and limits physicians in implementing prophylactic treatment approaches. This report presents an analytical approach to tissue characterization based on matrix-assisted laser desorption ionization time-of-flight (MALDI TOF) mass spectrometry imaging (MSI) which is introduced in an attempt to develop a reference database for predictive classification of brain tumors. This pilot study was designed to evaluate the potential of such an approach and to begin to address limitations of the current methodology. Five recurrent and progressive meningiomas for which surgical specimens were available from the original and progressed grades were selected and tested against nonprogressive high-grade meningiomas, high-grade gliomas, and nontumor brain specimens. The common profiling approach of data acquisition was compared to imaging and revealed significant benefits in spatially resolved acquisition for improved spectral definition. A preliminary classifier based on the support vector machine showed the ability to distinguish meningioma image spectra from the nontumor brain and from gliomas, a different type of brain tumor, and to enable class imaging of surgical tissue. Although the development of classifiers was shown to be sensitive to data preparation parameters such as recalibration and peak picking criteria, it also suggested the potential for maturing into a predictive algorithm if provided with a larger series of well-defined cases.

PMID:
20196536
PMCID:
PMC2852177
DOI:
10.1021/ac100113w
[Indexed for MEDLINE]
Free PMC Article

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