Non-invasive assessment of hepatic fat accumulation in chronic hepatitis C by 1H magnetic resonance spectroscopy

2010 ◽  
Vol 74 (3) ◽  
pp. e60-e66 ◽  
Author(s):  
Martin Krššák ◽  
Harald Hofer ◽  
Fritz Wrba ◽  
Martin Meyerspeer ◽  
Attila Brehm ◽  
...  
Keyword(s):  
Chronic Hepatitis ◽  
Hepatitis C ◽  
Magnetic Resonance ◽  
Magnetic Resonance Spectroscopy ◽  
Chronic Hepatitis C ◽  
Resonance Spectroscopy ◽  
1H Magnetic Resonance Spectroscopy ◽  
Fat Accumulation ◽  
Non Invasive ◽  
Hepatic Fat

Neurometabolites Alterations Judged by Magnetic Resonance Spectroscopy may Underlie Chronic Hepatitis C associated Fatigue

2012 ◽  
Vol 3 (6) ◽  
pp. 357-363
Author(s):  
Abdel_Salam O.A Abdel_Salam O.A ◽  
◽  
Elshazly T.A Elshazly T.A ◽  
Amr M El-rabat ◽  
Ali A.M Ali A.M ◽  
...  
Keyword(s):  
Chronic Hepatitis ◽  
Hepatitis C ◽  
Magnetic Resonance ◽  
Magnetic Resonance Spectroscopy ◽  
Chronic Hepatitis C ◽  
Resonance Spectroscopy

Cerebral immune activation in chronic hepatitis C infection: A magnetic resonance spectroscopy study

2008 ◽  
Vol 49 (3) ◽  
pp. 316-322 ◽  
Author(s):  
Daniel M. Forton ◽  
Gavin Hamilton ◽  
Joanna M. Allsop ◽  
Vijay P. Grover ◽  
Keith Wesnes ◽  
...  
Keyword(s):  
Chronic Hepatitis ◽  
Hepatitis C ◽  
Magnetic Resonance ◽  
Magnetic Resonance Spectroscopy ◽  
Chronic Hepatitis C ◽  
Immune Activation ◽  
Resonance Spectroscopy ◽  
Spectroscopy Study ◽  
Hepatitis C Infection ◽  
Magnetic Resonance Spectroscopy Study

scholarly journals 1314: Ultrasound Quantitative Evaluation of Hepatic Fat Content: Comparison with 1H Magnetic Resonance Spectroscopy

2009 ◽  
Vol 35 (8) ◽  
pp. S185
Author(s):  
Marcello Mancini ◽  
Anna Prinster ◽  
Raffaele Liuzzi ◽  
Gianni Annuzzi ◽  
Rosalba Giacco ◽  
...  
Keyword(s):  
Magnetic Resonance ◽  
Magnetic Resonance Spectroscopy ◽  
Quantitative Evaluation ◽  
Fat Content ◽  
Resonance Spectroscopy ◽  
1H Magnetic Resonance Spectroscopy ◽  
Hepatic Fat

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.

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