Lipid Profile and Hepatic Fat Content Measured by 1H MR Spectroscopy in Patients before and after Liver Transplantation

Increased hepatic fat content (HFC) is a hallmark of non-alcoholic fatty liver (NAFL) disease, a common condition in liver transplant recipients. Proton MR spectroscopy (1H MRS) and MR imaging-based proton density fat fraction as the only diagnosis modality enable precise non-invasive measurement of HFC and, also, fatty acid profiles in vivo. Using 1H MRS at 3T, we examined 47 liver transplantation candidates and 101 liver graft recipients. A point-resolved spectroscopy sequence was used to calculate the steatosis grade along with the saturated, unsaturated and polyunsaturated fractions of fatty acids in the liver. The steatosis grade measured by MRS was compared with the histological steatosis grade. HFC, represented by fat fraction values, is adept at distinguishing non-alcoholic steatohepatitis (NASH), NAFL and non-steatotic liver transplant patients. Relative hepatic lipid saturation increases while unsaturation decreases in response to increased HFC. Additionally, relative hepatic lipid saturation increases while unsaturation and polyunsaturation both decrease in liver recipients with histologically proven post-transplant NASH or NAFL compared to non-steatotic patients. HFC, measured by in vivo 1H MRS, correlated well with histological results. 1H MRS is a simple and fast method for in vivo analysis of HFC and its composition. It provides non-invasive support for NAFL and NASH diagnoses.

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P1045 : Ultra-high-field MR-spectroscopy in NAFLD as non-invasive in-vivo tool for monitoring changes in fat and energy metabolism with potential identification of NASH and advanced fibrosis by saturation transfer technique

Journal of Hepatology ◽

10.1016/s0168-8278(15)31243-5 ◽

2015 ◽

Vol 62 ◽

pp. S739-S740 ◽

Cited By ~ 3

Author(s):

S. Traussnigg ◽

C. Kienbacher ◽

M. Gajdošík ◽

L. Valkovič ◽

H. Hofer ◽

...

Keyword(s):

Energy Metabolism ◽

Mr Spectroscopy ◽

High Field ◽

Advanced Fibrosis ◽

Saturation Transfer ◽

Ultra High Field ◽

Non Invasive ◽

High Field Mr

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Correlations of PPARα and PPARγ expressions with 1H-MRS quantified hepatic fat content in pups of rats that experienced intrauterine growth restriction

The Journal of Maternal-Fetal & Neonatal Medicine ◽

10.1080/14767058.2019.1611766 ◽

2019 ◽

Vol 34 (4) ◽

pp. 606-613

Author(s):

Tao Wang ◽

Pingyang Chen ◽

Dujun Bian ◽

Enhua Xiao ◽

Dan Li

Keyword(s):

Intrauterine Growth Restriction ◽

Fat Content ◽

Growth Restriction ◽

1H Mrs ◽

Intrauterine Growth ◽

Hepatic Fat

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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 ◽

10.3390/cancers12113406 ◽

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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