2010
DOI: 10.1002/mrm.22619
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Ex vivo high resolution magic angle spinning metabolic profiles describe intratumoral histopathological tissue properties in adult human gliomas

Abstract: In gliomas one can observe distinct histopathological tissue properties, such as viable tumor cells, necrotic tissue or regions where the tumor infiltrates normal brain. A first screening between the different intratumoral histopathological tissue properties would greatly assist in correctly diagnosing and prognosing gliomas. The potential of ex vivo high resolution magic angle spinning spectroscopy in characterizing these properties is analyzed and the biochemical differences between necrosis, high cellularit… Show more

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Cited by 18 publications
(17 citation statements)
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“…Recent work using HRMAS for brain tumors showed that it is possible to classify spectroscopy samples according to tumor histological type [8, 12, 16, 18, 20, 22, 27, 44, 46] and grade [10, 12, 14, 16, 27, 46, 47] using multivariate methods such as linear discriminant analysis [14, 16], support vector machines [12, 14, 16], logistic regression [10, 47], partial least squares discriminant analysis [8], and multi-layer perceptrons [12]. Moreover, HRMAS multivariate studies successfully revealed the status of tumor microheterogeneity [9, 13, 15, 17] and detected alterations in tumor metabolism before changes in morphology occurred [12]. These studies combined dimensionality reduction methods such as principal component analysis [14, 20, 22] and metabolite quantification [10, 14–17, 22, 30, 47] with the robust classification methods listed above.…”
Section: Related Workmentioning
confidence: 99%
“…Recent work using HRMAS for brain tumors showed that it is possible to classify spectroscopy samples according to tumor histological type [8, 12, 16, 18, 20, 22, 27, 44, 46] and grade [10, 12, 14, 16, 27, 46, 47] using multivariate methods such as linear discriminant analysis [14, 16], support vector machines [12, 14, 16], logistic regression [10, 47], partial least squares discriminant analysis [8], and multi-layer perceptrons [12]. Moreover, HRMAS multivariate studies successfully revealed the status of tumor microheterogeneity [9, 13, 15, 17] and detected alterations in tumor metabolism before changes in morphology occurred [12]. These studies combined dimensionality reduction methods such as principal component analysis [14, 20, 22] and metabolite quantification [10, 14–17, 22, 30, 47] with the robust classification methods listed above.…”
Section: Related Workmentioning
confidence: 99%
“…The best separations among the three groups were enabled by tCho/NAA and Lip/NAA ratios, with the latter being especially useful in the differentiation between necrotic and highly cellular tissue. The necrotic tissue was characterized by negligible NAA and tCho but very high quantities of lipids, whereas highly cellular tissue showed low NAA and lipids but huge amounts of tCho, a marker of cell membrane turnover . Furthermore, 2HG was found to be positively correlated with mitotic activity (measured by the MIB‐1 index), cellular density and relative tumor content within the tissue sample …”
Section: Applications Of Hrmas Mrs In Studies Of Human Malignant Disementioning
confidence: 94%
“…Accurate diagnosis of GBMs is of great importance for guiding therapy and planning operations. Being different from other brain tumors which present similar spectral patterns, GBMs are characterized by high infiltration [1, 2]. Such characteristic brings huge difficulty in tumor typing and diagnosis.…”
Section: Introductionmentioning
confidence: 99%
“…NMF [11] is an alternative blind source separation technique with only nonnegative constraint. It has shown great potentials in brain tissue differentiation [2, 1214]. In our previous work, we proposed an unsupervised method, namely, hierarchical nonnegative matrix factorization (hNMF), to interpret the MRSI data for GBMs without prior knowledge and provided an easy way to interpret MRSI data of GBMs for each tissue type [15].…”
Section: Introductionmentioning
confidence: 99%