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Three Tesla magnetic resonance imaging detects oxalate osteopathy in patients with primary hyperoxaluria type I
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Three Tesla magnetic resonance imaging detects oxalate osteopathy in patients with primary hyperoxaluria type I
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Three Tesla magnetic resonance imaging detects oxalate osteopathy in patients with primary hyperoxaluria type I
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Journal Article
Three Tesla magnetic resonance imaging detects oxalate osteopathy in patients with primary hyperoxaluria type I
2023
Overview
BackgroundWith 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.MethodsWe 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.ResultsIndependent 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.ConclusionsMRI 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.
