Graphical representation of the Mayo Image Classification for autosomal dominant polycystic kidney disease. The chart plots height-adjusted total kidney volume against patient age and categorizes patients into classes 1A through 1E based on estimated annual kidney growth rates. Colored trend lines represent increasing disease severity, from Class 1A (slow growth) to Class 1E (rapid growth). MRI scans on the right show representative kidney appearances for different Mayo Image Classification classes, demonstrating progressively larger kidneys and increasing cyst burden with more severe disease categories. Mayo Imaging Classification for patients with autosomal dominant polycystic kidney disease

The Mayo Image Classification subclassifies patients with Class 1 autosomal dominant polycystic kidney disease according to age-adjusted, height-adjusted total kidney volume. Classification categories are defined based on estimated annual kidney growth rates of less than or equal to 1.5%, 1.5% to 3%, 3% to 4.5%, 4.5% to 6% and greater than 6%.

Predictive models and imaging biomarkers in autosomal dominant polycystic kidney disease

The lab's research focuses on developing and refining imaging- and biomarker-based approaches to improve risk stratification and predict disease progression in autosomal dominant polycystic kidney disease. Dr. Irazabal developed the Mayo Image Classification for autosomal dominant polycystic kidney disease. This classification system is now widely used nationally and internationally for clinical trial design and risk stratification in patients with autosomal dominant polycystic kidney disease.

Research continues to evaluate advanced imaging techniques and novel biomarkers. The goal is to improve early assessment of disease progression and therapeutic response.

Areas of ongoing investigation include:

  • Novel imaging biomarkers.
  • Noninvasive assessment of kidney parenchyma.
  • Imaging approaches to characterize renal microvascular abnormalities.
  • Integration of imaging with vascular and metabolic biomarkers.
  • Longitudinal disease modeling and progression prediction.

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