Molecular classification of brain tumor biopsies using solid-state magic angle spinning proton magnetic resonance spectroscopy and robust classifiers
| dc.contributor.author | Andronesi, O. C. | en |
| dc.contributor.author | Blekas, K. | en |
| dc.contributor.author | Mintzopoulos, D. | en |
| dc.contributor.author | Astrakas, L. | en |
| dc.contributor.author | Black, P. M. | en |
| dc.contributor.author | Tzika, A. A. | en |
| dc.date.accessioned | 2015-11-24T17:01:42Z | |
| dc.date.available | 2015-11-24T17:01:42Z | |
| dc.identifier.issn | 1019-6439 | - |
| dc.identifier.uri | https://olympias.lib.uoi.gr/jspui/handle/123456789/10965 | |
| dc.rights | Default Licence | - |
| dc.subject | brain/cns cancers | en |
| dc.subject | tumor biomarkers | en |
| dc.subject | ex vivo high-resolution magic angle spinning magnetic resonance spectroscopy | en |
| dc.subject | support vector machines | en |
| dc.subject | neural networks | en |
| dc.subject | h-1 mr spectra | en |
| dc.subject | primitive neuroectodermal tumor | en |
| dc.subject | gene-expression signatures | en |
| dc.subject | artificial neural-networks | en |
| dc.subject | support-vector-machines | en |
| dc.subject | short echo time | en |
| dc.subject | in-vivo | en |
| dc.subject | ex-vivo | en |
| dc.subject | pattern-recognition | en |
| dc.subject | nmr-spectroscopy | en |
| dc.title | Molecular classification of brain tumor biopsies using solid-state magic angle spinning proton magnetic resonance spectroscopy and robust classifiers | en |
| heal.abstract | Brain tumors are one of the leading causes of death in adults with cancer; however, molecular classification of these tumors with in vivo magnetic resonance spectroscopy (MRS) is limited because of the small number of metabolites detected. In vitro MRS provides highly informative biomarker profiles at higher fields, but also consumes the sample so that it is unavailable for subsequent analysis. In contrast, ex vivo high-resolution magic angle spinning (HRMAS) MRS conserves the sample but requires large samples and can pose technical challenges for producing accurate data, depending on the sample testing temperature. We developed a novel approach that combines a two-dimensional (213), solid-state, HRMAS proton ((1)H) NMR method, TOBSY (total through-bond spectroscopy), which maximizes the advantages of HRMAS and a robust classification strategy. We used similar to 2 mg of tissue at -8 degrees C from each of 55 brain biopsies, and reliably detected 16 different biologically relevant molecular species. We compared two classification strategies, the support vector machine (SVM) classifier and a feed-forward neural network using the Levenberg-Marquardt back-propagation algorithm. We used the minimum redundancy/maximum relevance (MRMR) method as a powerful feature-selection scheme along with the SVM classifier. We suggest that molecular characterization of brain tumors based on highly informative 2D MRS should enable us to type and prognose even inoperable patients with high accuracy in vivo. | en |
| heal.access | campus | - |
| heal.fullTextAvailability | TRUE | - |
| heal.identifier.primary | Doi 10.3892/Ijo_00000090 | - |
| heal.journalName | Int J Oncol | en |
| heal.journalType | peer reviewed | - |
| heal.language | en | - |
| heal.publicationDate | 2008 | - |
| heal.recordProvider | Πανεπιστήμιο Ιωαννίνων. Σχολή Θετικών Επιστημών. Τμήμα Μηχανικών Ηλεκτρονικών Υπολογιστών και Πληροφορικής | el |
| heal.type | journalArticle | - |
| heal.type.el | Άρθρο Περιοδικού | el |
| heal.type.en | Journal article | en |
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