Multiparametric MR imaging of sinonasal diseases: time-signal intensity curve- and apparent diffusion coefficient-based differentiation between benign and malignant lesions

AJNR Am J Neuroradiol. 2011 Dec;32(11):2154-9. doi: 10.3174/ajnr.A2675. Epub 2011 Sep 15.

Abstract

Background and purpose: The sinonasal region is a platform for a broad spectrum of benign and malignant diseases, and image-based differentiation between benign and malignant diseases in this area is often difficult. Here, we evaluated multiparametric MR imaging with combined use of TICs and ADCs for the differentiation between benign and malignant sinonasal tumors and tumorlike diseases.

Materials and methods: TICs obtained from dynamic contrast-enhanced MR imaging and ADCs were analyzed on a lesion-by-lesion (overall TIC and ADC) and pixel-by-pixel (TIC and ADC mapping) basis in patients with benign (n = 21) or malignant (n = 23) sinonasal tumors and tumorlike diseases. The TICs were semiautomatically classified into 5 distinctive patterns (flat, slow uptake, rapid uptake with low washout ratio, rapid uptake with high washout ratio, and miscellaneous). ADCs were determined by using b-values of 500 and 1000 s/mm(2).

Results: Malignant sinonasal tumors had small (<25%) areas of the type 1 flat TIC profile as determined by pixel-by-pixel TIC analysis and large (≥50%) areas of low or extremely low ADCs (≤1.2 × 10(-3) mm(2/)s) as determined by ADC mapping. Consequently, stepwise classification on the basis of TICs and ADCs successfully (at 100% accuracy) discriminated malignant from benign sinonasal diseases in the present patient cohort.

Conclusions: Multiparametric MR imaging by using TICs and ADCs may help differentiate benign and malignant sinonasal diseases.

MeSH terms

  • Algorithms*
  • Diagnosis, Differential
  • Diffusion Magnetic Resonance Imaging / methods*
  • Female
  • Humans
  • Image Enhancement / methods
  • Image Interpretation, Computer-Assisted / methods*
  • Male
  • Middle Aged
  • Paranasal Sinus Neoplasms / pathology*
  • Reproducibility of Results
  • Sensitivity and Specificity