Three Tesla magnetic resonance imaging detects oxalate osteopathy in patients with primary hyperoxaluria type I

Background With declining kidney function and therefore increasing plasma oxalate, patients with primary hyperoxaluria type I (PHI) are at risk to systemically deposit calcium-oxalate crystals. This systemic oxalosis may occur even at early stages of chronic kidney failure (CKD) but is difficult to detect with non-invasive imaging procedures. Methods We tested if magnetic resonance imaging (MRI) is sensitive to detect oxalate deposition in bone. A 3 Tesla MRI of the left knee/tibial metaphysis was performed in 46 patients with PHI and in 12 healthy controls. In addition to the investigator's interpretation, signal intensities (SI) within a region of interest (ROI, transverse images below the level of the physis in the proximal tibial metaphysis) were measured pixelwise, and statistical parameters of their distribution were calculated. In addition, 52 parameters of texture analysis were evaluated. Plasma oxalate and CKD status were correlated to MRI findings. MRI was then implemented in routine practice. Results Independent interpretation by investigators was consistent in most cases and clearly differentiated patients from controls. Statistically significant differences were seen between patients and controls (p < 0.05). No correlation/relation between the MRI parameters and CKD stages or Pox levels was found. However, MR imaging of oxalate osteopathy revealed changes attributed to clinical status which differed clearly to that in secondary hyperparathyroidism. Conclusions MRI is able to visually detect (early) oxalate osteopathy in PHI. It can be used for its monitoring and is distinguished from renal osteodystrophy. In the future, machine learning algorithms may aid in the objective assessment of oxalate deposition in bone

Diagnostic value of magnetic resonance parametric mapping for non-invasive assessment of liver fibrosis in patients with primary sclerosing cholangitis

Background!#!Primary sclerosing cholangitis (PSC) is a chronic cholestatic liver disease, characterized by bile duct inflammation and destruction, leading to biliary fibrosis and cirrhosis. The purpose of this study was to investigate the utility of T1 and T2 mapping parameters, including extracellular volume fraction (ECV) for non-invasive assessment of fibrosis severity in patients with PSC.!##!Methods!#!In this prospective study, patients with PSC diagnosis were consecutively enrolled from January 2019 to July 2020 and underwent liver MRI. Besides morphological sequences, MR elastography (MRE), and T1 and T2 mapping were performed. ECV was calculated from T1 relaxation times. The presence of significant fibrosis (≥ F2) was defined as MRE-derived liver stiffness ≥ 3.66 kPa and used as the reference standard, against which the diagnostic performance of MRI mapping parameters was tested. Student t test, ROC analysis and Pearson correlation were used for statistical analysis.!##!Results!#!32 patients with PSC (age range 19-77 years) were analyzed. Both, hepatic native T1 (r = 0.66; P &amp;lt; 0.001) and ECV (r = 0.69; P &amp;lt; 0.001) correlated with MRE-derived liver stiffness. To diagnose significant fibrosis (≥ F2), ECV revealed a sensitivity of 84.2% (95% confidence interval (CI) 62.4-94.5%) and a specificity of 84.6% (CI 57.8-95.7%); hepatic native T1 revealed a sensitivity of 52.6% (CI 31.7-72.7%) and a specificity of 100.0% (CI 77.2-100.0%). Hepatic ECV (area under the curve (AUC) 0.858) and native T1 (AUC 0.711) had an equal or higher diagnostic performance for the assessment of significant fibrosis compared to serologic fibrosis scores (APRI (AUC 0.787), FIB-4 (AUC 0.588), AAR (0.570)).!##!Conclusions!#!Hepatic T1 and ECV can diagnose significant fibrosis in patients with PSC. Quantitative mapping has the potential to be a new non-invasive biomarker for liver fibrosis assessment and quantification in PSC patients

Linearity, Bias, and Precision of Hepatic Proton Density Fat Fraction Measurements by Using MR Imaging: A Meta-Analysis.

Purpose To determine the linearity, bias, and precision of hepatic proton density fat fraction (PDFF) measurements by using magnetic resonance (MR) imaging across different field strengths, imager manufacturers, and reconstruction methods. Materials and Methods This meta-analysis was performed in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. A systematic literature search identified studies that evaluated the linearity and/or bias of hepatic PDFF measurements by using MR imaging (hereafter, MR imaging-PDFF) against PDFF measurements by using colocalized MR spectroscopy (hereafter, MR spectroscopy-PDFF) or the precision of MR imaging-PDFF. The quality of each study was evaluated by using the Quality Assessment of Studies of Diagnostic Accuracy 2 tool. De-identified original data sets from the selected studies were pooled. Linearity was evaluated by using linear regression between MR imaging-PDFF and MR spectroscopy-PDFF measurements. Bias, defined as the mean difference between MR imaging-PDFF and MR spectroscopy-PDFF measurements, was evaluated by using Bland-Altman analysis. Precision, defined as the agreement between repeated MR imaging-PDFF measurements, was evaluated by using a linear mixed-effects model, with field strength, imager manufacturer, reconstruction method, and region of interest as random effects. Results Twenty-three studies (1679 participants) were selected for linearity and bias analyses and 11 studies (425 participants) were selected for precision analyses. MR imaging-PDFF was linear with MR spectroscopy-PDFF (R2 = 0.96). Regression slope (0.97; P &lt; .001) and mean Bland-Altman bias (-0.13%; 95% limits of agreement: -3.95%, 3.40%) indicated minimal underestimation by using MR imaging-PDFF. MR imaging-PDFF was precise at the region-of-interest level, with repeatability and reproducibility coefficients of 2.99% and 4.12%, respectively. Field strength, imager manufacturer, and reconstruction method each had minimal effects on reproducibility. Conclusion MR imaging-PDFF has excellent linearity, bias, and precision across different field strengths, imager manufacturers, and reconstruction methods. © RSNA, 2017 Online supplemental material is available for this article. An earlier incorrect version of this article appeared online. This article was corrected on October 2, 2017