Two-dimensional correlated spectroscopy distinguishes clear cell renal cell carcinoma from other kidney neoplasms and non-cancer kidney.

Background: Routinely used clinical scanners, such as computed tomography (CT), magnetic resonance imaging (MRI) and ultrasound (US), are unable to distinguish between aggressive and indolent tumor subtypes in masses localized to the kidney, often leading to surgical overtreatment. The results of the current investigation demonstrate that chemical differences, detected in human kidney biopsies using two-dimensional COrrelated SpectroscopY (2D L-COSY) and evaluated using multivariate statistical analysis, can distinguish these subtypes. Methods: One hundred and twenty-six biopsy samples from patients with a confirmed enhancing kidney mass on abdominal imaging were analyzed as part of the training set. A further forty-three samples were used for model validation. In patients undergoing radical nephrectomy, biopsies of non-cancer kidney cortical tissue were also collected as a non-cancer control group. Spectroscopy data were analyzed using multivariate statistical analysis, including principal component analysis (PCA) and orthogonal projection to latent structures with discriminant analysis (OPLS-DA), to identify biomarkers in kidney cancer tissue that was also classified using the gold-standard of histopathology. Results: The data analysis methodology showed good separation between clear cell renal cell carcinoma (ccRCC) versus non-clear cell RCC (non-ccRCC) and non-cancer cortical tissue from the kidneys of tumor-bearing patients. Variable Importance for the Projection (VIP) values, and OPLS-DA loadings plots were used to identify chemical species that correlated significantly with the histopathological classification. Model validation resulted in the correct classification of 37/43 biopsy samples, which included the correct classification of 15/17 ccRCC biopsies, achieving an overall predictive accuracy of 86%, Those chemical markers with a VIP value >1.2 were further analyzed using univariate statistical analysis. A subgroup analysis of 47 tumor tissues arising from T1 tumors revealed distinct separation between ccRCC and non-ccRCC tissues. Conclusions: This study provides metabolic insights that could have future diagnostic and/or clinical value. The results of this work demonstrate a clear separation between clear cell and non-ccRCC and non-cancer kidney tissue from tumor-bearing patients. The clinical translation of these results will now require the development of a one-dimensional (1D) magnetic resonance spectroscopy (MRS) protocol, for the kidney, using an in vivo clinical MRI scanner.

Quantification of ß-Amyloidosis and rCBF with Dedicated PET, 7 T MR Imaging, and High-Resolution Microscopic MR Imaging at 16.4 T in APP23 Mice.

UNLABELLED: We present a combined PET/7 T MR imaging and 16.4 T microscopic MR imaging dual-modality imaging approach enabling quantification of the amyloid load at high sensitivity and high resolution, and of regional cerebral blood flow (rCBF) in the brain of transgenic APP23 mice. Moreover, we demonstrate a novel, voxel-based correlative data analysis method for in-depth evaluation of amyloid PET and rCBF data. METHODS: We injected 11C-Pittsburgh compound B (PIB) intravenously in transgenic and control APP23 mice and performed dynamic PET measurements. rCBF data were recorded with a flow-sensitive alternating inversion recovery approach at 7 T. Subsequently, the animals were sacrificed and their brains harvested for ex vivo microscopic MR imaging at 16.4 T with a T2*-weighted gradient-echo sequence at 30-µm spatial resolution. Additionally, correlative amyloid histology was performed. The 11C-PIB PET data were quantified to nondisplaceable binding potentials (BPND) using the Logan graphical analysis; flow-sensitive alternating inversion recovery data were quantified with a simplified version of the Bloch equation. RESULTS: Amyloid load assessed by both 11C-PIB PET and amyloid histology was highest in the frontal cortex of transgenic mice (11C-PIB BPND: 0.93±0.08; amyloid histology: 15.1%±1.5%), followed by the temporoparietal cortex (11C-PIB BPND: 0.75±0.08; amyloid histology: 13.9%±0.7%) and the hippocampus (11C-PIB BPND: 0.71±0.09; amyloid histology: 9.2%±0.9%), and was lowest in the thalamus (11C-PIB BPND: 0.40±0.07; amyloid histology: 6.6%±0.6%). However, 11C-PIB BPND and amyloid histology linearly correlated (R2=0.82, P<0.05) and were significantly higher in transgenic animals (P<0.01). Similarly, microscopic MR imaging allowed quantifying the amyloid load, in addition to the detection of substructures within single amyloid plaques correlating with amyloid deposition density and the measurement of hippocampal atrophy. Finally, we found an inverse relationship between 11C-PIB BPND and rCBF MR imaging in the voxel-based analysis that was absent in control mice (slopetg: -0.11±0.03; slopeco: 0.004±0.005; P=0.014). CONCLUSION: Our dual-modality imaging approach using 11C-PIB PET/7 T MR imaging and 16.4 T microscopic MR imaging allowed amyloid-load quantification with high sensitivity and high resolution, the identification of substructures within single amyloid plaques, and the quantification of rCBF.

Magnetic resonance imaging and spectroscopy accurately estimate the severity of steatosis provided the stage of fibrosis is considered.

BACKGROUND/AIMS: Currently the diagnosis and severity of hepatic steatosis can be established accurately only by liver biopsy. Previous small studies found that steatosis measured by magnetic resonance spectroscopy (MRS) and imaging (MRI) correlated with histological assessment of liver triglyceride content. However, the accuracy of MRS/MRI for grading the severity of steatosis has not been addressed. The aims of this study were (1) to determine whether MRS and MRI can discriminate grades of steatosis in a large cohort of consecutive patients with a wide spectrum of liver disease aetiology and severity (2) to evaluate the effect of hepatic fibrosis, inflammation and iron on quantitation of intrahepatocellular lipid (IHCL) by these techniques. METHODS: Ninety-four sequential patients who underwent percutaneous liver biopsy or liver resection had MRS and MRI (Dixon in phase/out of phase (Dixon IP/OP) and with/without fat saturation (+/-FS) images) to determine IHCL. Histology was used as the reference standard. RESULTS: Close relationships were observed between the percentage of steatosis estimated by histology and MRS/MRI (r(s)=0.88 p<0.001 for all techniques). However, separate equations were required for the percentage of steatosis to avoid underestimation by imaging for patients with moderate or advanced fibrosis. All techniques had good diagnostic accuracy for mild steatosis (AUROC > or =0.87) as well as moderate/severe steatosis (AUROC > or =0.89). Hepatic inflammation and mild iron deposition (Perls' grade 1 and 2) did not interfere with estimation of steatosis by imaging. CONCLUSIONS: MRS and MRI had good accuracy for grading the severity of steatosis in subjects with liver disease, provided that stage of fibrosis was considered.

Magnetic resonance imaging and spectroscopy for monitoring liver steatosis.

PURPOSE: To compare noninvasive MRI and magnetic resonance spectroscopy (MRS) methods with liver biopsy to quantify liver fat content. MATERIALS AND METHODS: Quantification of liver fat was compared by liver biopsy, proton MRS, and MRI using in-phase/out-of-phase (IP/OP) and plus/minus fat saturation (+/-FS) techniques. The reproducibility of each MR measure was also determined. An additional group of overweight patients with steatosis underwent hepatic MRI and MRS before and after a six-month weight-loss program. RESULTS: A close correlation was demonstrated between histological assessment of steatosis and measurement of intrahepatocellular lipid (IHCL) by MRS (r(s) = 0.928, P < 0.0001) and MRI (IP/OP r(s) = 0.942, P < 0.0001; FS r(s) = 0.935, P < 0.0001). Following weight reduction, four of five patients with >5% weight loss had a decrease in IHCL of >or=50%. CONCLUSION: These findings suggest that standard MRI protocols provide a rapid, safe, and quantitative assessment of hepatic steatosis. This is important because MRS is not available on all clinical MRI systems. This will enable noninvasive monitoring of the effects of interventions such as weight loss or pharmacotherapy in patients with fatty liver diseases.