European Radiology

, Volume 18, Issue 2, pp 365–375

MR classification of renal masses with pathologic correlation

Authors

    • Department of RadiologyBeth Israel Deaconess Medical Center
  • Mary T. Chou
    • Department of RadiologyBeth Israel Deaconess Medical Center
  • Long Ngo
    • Department of General Medicine and Primary CareBeth Israel Deaconess Medical Center
  • Ronaldo H. Baroni
    • Department of RadiologyBeth Israel Deaconess Medical Center
  • Elizabeth M. Genega
    • Department of PathologyBeth Israel Deaconess Medical Center
  • Laura Galaburda
    • Department of PathologyBeth Israel Deaconess Medical Center
  • William C. DeWolf
    • Division of Urology, Department of SurgeryBeth Israel Deaconess Medical Center
  • Neil M. Rofsky
    • Department of RadiologyBeth Israel Deaconess Medical Center
Urogenital

DOI: 10.1007/s00330-007-0757-0

Cite this article as:
Pedrosa, I., Chou, M.T., Ngo, L. et al. Eur Radiol (2008) 18: 365. doi:10.1007/s00330-007-0757-0

Abstract

To perform a feature analysis of malignant renal tumors evaluated with magnetic resonance (MR) imaging and to investigate the correlation between MR imaging features and histopathological findings. MR examinations in 79 malignant renal masses were retrospectively evaluated, and a feature analysis was performed. Each renal mass was assigned to one of eight categories from a proposed MRI classification system. The sensitivity and specificity of the MRI classification system to predict the histologic subtype and nuclear grade was calculated. Subvoxel fat on chemical shift imaging correlated to clear cell type (p < 0.05); sensitivity = 42%, specificity = 100%. Large size, intratumoral necrosis, retroperitoneal vascular collaterals, and renal vein thrombosis predicted high-grade clear cell type (p < 0.05). Small size, peripheral location, low intratumoral SI on T2-weighted images, and low-level enhancement were associated with low-grade papillary carcinomas (p < 0.05). The sensitivity and specificity of the MRI classification system for diagnosing low grade clear cell, high-grade clear cell, all clear cell, all papillary, and transitional carcinomas were 50% and 94%, 93% and 75%, 92% and 83%, 80% and 94%, and 100% and 99%, respectively. The MRI feature analysis and proposed classification system help predict the histological type and nuclear grade of renal masses.

Keywords

Kidney neoplasmsCarcinomaRenal cellMagnetic resonance imagingMultivariate analysis

Copyright information

© European Society of Radiology 2007