Differentiation of Clear Cell Renal Cell Carcinoma From Other Renal Cortical Tumors by Use of a Quantitative Multiparametric MRI Approach. Academic Article uri icon

Overview

abstract

  • OBJECTIVE: The purpose of this study was to develop a quantitative multiparametric MRI approach to differentiating clear cell renal cell carcinoma (RCC) from other renal cortical tumors. MATERIALS AND METHODS: This retrospective study included 119 patients with 124 histopathologically confirmed renal cortical tumors who underwent preoperative MRI including DWI, contrast-enhanced, and chemical-shift sequences before nephrectomy. Two radiologists independently assessed each tumor volumetrically, and apparent diffusion coefficient values, parameters from multiphasic contrast-enhanced MRI (peak enhancement, upslope, downslope, AUC), and chemical-shift indexes were calculated. Univariate and multivariable logistic regression analyses were performed to identify parameters associated with clear cell RCC. RESULTS: Interreader agreement was excellent (intraclass correlation coefficient, 0.815-0.994). The parameters apparent diffusion coefficient (reader 1 AUC, 0.804; reader 2, 0.807), peak enhancement (reader 1 AUC, 0.629; reader 2, 0.606), and downslope (reader 1 AUC, 0.575; reader 2, 0.561) were significantly associated with discriminating clear cell RCC from other renal cortical tumors. The combination of all three parameters further increased diagnostic accuracy (reader 1 AUC, 0.889; reader 2, 0.907; both p ≤ 0.001), yielding sensitivities of 0.897 for reader 1 and 0.897 for reader 2, and specificities of 0.762 for reader 1 and 0.738 for reader 2 in the identification of clear cell RCC. With maximized sensitivity, specificities of 0.429 and 0.262 were reached for readers 1 and 2, respectively. CONCLUSION: A quantitative multiparametric approach statistically significantly improves diagnostic performance in differentiating clear cell RCC from other renal cortical tumors.

publication date

  • January 17, 2017

Research

keywords

  • Algorithms
  • Carcinoma, Renal Cell
  • Image Interpretation, Computer-Assisted
  • Kidney Neoplasms
  • Magnetic Resonance Imaging
  • Pattern Recognition, Automated

Identity

PubMed Central ID

  • PMC5441564

Scopus Document Identifier

  • 85013646820

Digital Object Identifier (DOI)

  • 10.2214/AJR.16.16652

PubMed ID

  • 28095036

Additional Document Info

volume

  • 208

issue

  • 3