1H magnetic resonance spectroscopy of preinvasive and invasive cervical cancer: In vivo-ex vivo profiles and effect of tumor load

2004 ◽  
Vol 19 (3) ◽  
pp. 356-364 ◽  
Author(s):  
Marrita M. Mahon ◽  
I. Jane Cox ◽  
Roberto Dina ◽  
W. Patrick Soutter FRCOG ◽  
G. Angus McIndoe MRCOG ◽  
...  
Keyword(s):  
Cervical Cancer ◽  
Magnetic Resonance ◽  
Magnetic Resonance Spectroscopy ◽  
Ex Vivo ◽  
Invasive Cervical Cancer ◽  
Resonance Spectroscopy ◽  
1H Magnetic Resonance Spectroscopy ◽  
Tumor Load

1 H magnetic resonance spectroscopy of invasive cervical cancer: an in vivo study with ex vivo corroboration

2004 ◽  
Vol 17 (1) ◽  
pp. 1-9 ◽  
Author(s):  
Marrita M. Mahon ◽  
Andreanna D. Williams ◽  
W. Patrick Soutter ◽  
I. Jane Cox ◽  
G. Angus McIndoe ◽  
...  
Keyword(s):  
Cervical Cancer ◽  
Magnetic Resonance ◽  
Magnetic Resonance Spectroscopy ◽  
Ex Vivo ◽  
Invasive Cervical Cancer ◽  
In Vivo Study ◽  
Resonance Spectroscopy

scholarly journals 1H magnetic resonance spectroscopy of 2H-to-1H exchange quantifies the dynamics of cellular metabolism in vivo

2020 ◽  
Vol 4 (3) ◽  
pp. 335-342 ◽  
Author(s):  
Laurie J. Rich ◽  
Puneet Bagga ◽  
Neil E. Wilson ◽  
Mitchell D. Schnall ◽  
John A. Detre ◽  
...  
Keyword(s):  
Magnetic Resonance ◽  
Magnetic Resonance Spectroscopy ◽  
Cellular Metabolism ◽  
Resonance Spectroscopy ◽  
1H Magnetic Resonance Spectroscopy

scholarly journals Non-Invasive Prediction of IDH Mutation in Patients with Glioma WHO II/III/IV Based on F-18-FET PET-Guided In Vivo 1H-Magnetic Resonance Spectroscopy and Machine Learning

Cancers ◽  
2020 ◽  
Vol 12 (11) ◽  
pp. 3406
Author(s):  
Elisabeth Bumes ◽  
Fro-Philip Wirtz ◽  
Claudia Fellner ◽  
Jirka Grosse ◽  
Dirk Hellwig ◽  
...  
Keyword(s):  
Machine Learning ◽  
Magnetic Resonance ◽  
Magnetic Resonance Spectroscopy ◽  
Support Vector ◽  
Resonance Spectroscopy ◽  
Idh Mutation ◽  
1H Mrs ◽  
1H Magnetic Resonance Spectroscopy ◽  
Non Invasive

Isocitrate dehydrogenase (IDH)-1 mutation is an important prognostic factor and a potential therapeutic target in glioma. Immunohistological and molecular diagnosis of IDH mutation status is invasive. To avoid tumor biopsy, dedicated spectroscopic techniques have been proposed to detect D-2-hydroxyglutarate (2-HG), the main metabolite of IDH, directly in vivo. However, these methods are technically challenging and not broadly available. Therefore, we explored the use of machine learning for the non-invasive, inexpensive and fast diagnosis of IDH status in standard 1H-magnetic resonance spectroscopy (1H-MRS). To this end, 30 of 34 consecutive patients with known or suspected glioma WHO grade II-IV were subjected to metabolic positron emission tomography (PET) imaging with O-(2-18F-fluoroethyl)-L-tyrosine (18F-FET) for optimized voxel placement in 1H-MRS. Routine 1H-magnetic resonance (1H-MR) spectra of tumor and contralateral healthy brain regions were acquired on a 3 Tesla magnetic resonance (3T-MR) scanner, prior to surgical tumor resection and molecular analysis of IDH status. Since 2-HG spectral signals were too overlapped for reliable discrimination of IDH mutated (IDHmut) and IDH wild-type (IDHwt) glioma, we used a nested cross-validation approach, whereby we trained a linear support vector machine (SVM) on the complete spectral information of the 1H-MRS data to predict IDH status. Using this approach, we predicted IDH status with an accuracy of 88.2%, a sensitivity of 95.5% (95% CI, 77.2–99.9%) and a specificity of 75.0% (95% CI, 42.9–94.5%), respectively. The area under the curve (AUC) amounted to 0.83. Subsequent ex vivo 1H-nuclear magnetic resonance (1H-NMR) measurements performed on metabolite extracts of resected tumor material (eight specimens) revealed myo-inositol (M-ins) and glycine (Gly) to be the major discriminators of IDH status. We conclude that our approach allows a reliable, non-invasive, fast and cost-effective prediction of IDH status in a standard clinical setting.

scholarly journals Proton Magnetic Resonance Spectroscopy Characterization of Rathke’s Cleft Cysts (RCCs): Relevance to the Differential Diagnosis of Pituitary Adenomas and RCCs

Cancers ◽  
2020 ◽  
Vol 12 (2) ◽  
pp. 360
Author(s):  
Omkar B. Ijare ◽  
Martyn A. Sharpe ◽  
David S. Baskin ◽  
Kumar Pichumani
Keyword(s):  
Differential Diagnosis ◽  
Magnetic Resonance ◽  
Magnetic Resonance Spectroscopy ◽  
Pituitary Adenomas ◽  
Ex Vivo ◽  
Medical Professionals ◽  
Resonance Spectroscopy ◽  
Rathke's Cleft Cysts ◽  
Rathke's Cleft

Background: Rathke’s Cleft Cysts (RCCs) are rare epithelial cysts arising from remnants of the Rathke pouch in the pituitary gland. A subset of these lesions enlarge and produce a mass effect with consequent hypopituitarism, and may result in visual loss. Moreover, some RCCs with a high intra-cystic protein content may mimic cystic pituitary adenoma, which makes their differential diagnosis ambiguous. Currently, medical professionals have no definitive way to distinguish RCCs from pituitary adenomas. Therefore, preoperative confirmation of RCCs would be of help to medical professionals for the management and proper surgical decision making. The goal of this study is to identify molecular markers in RCCs. Methods: We characterized aqueous and chloroform extracts of surgically resected RCCs and pituitary adenomas using ex vivo 1H NMR spectroscopy. Results: All RCCs exclusively showed the presence of mucopolysaccharides which are glycosaminoglycans (GAGs) made up of disaccharides of aminosugars and uronic sugars. Conclusion: GAGs can be used as metabolite marker for the detection of RCCs and this knowledge will lay the groundwork for the development of a non-invasive, in vivo magnetic resonance spectroscopy methodology for the differential diagnosis of RCCs and pituitary adenomas using clinical MRI scanners.

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