Role of diffusion-weighted magnetic resonance imaging in the diagnosis of gallbladder cancer

J Magn Reson Imaging. 2013 Jul;38(1):127-37. doi: 10.1002/jmri.23956. Epub 2012 Dec 19.

Abstract

Purpose: To determine the additional diagnostic value of high b-value diffusion-weighted imaging (DWI) compared to conventional biliary magnetic resonance imaging (MRI) for differentiating gallbladder (GB) cancer from benign GB diseases with wall thickening.

Materials and methods: Thirty-nine patients with GB cancers and 36 patients with cholecystitis having preoperative biliary MRIs were included. All patients underwent unenhanced T1- and T2-weighted imaging (T2WI), Gd-enhanced dynamic MRI, and DWI (b values 0, 100, 500, 1000 s/mm(2) ). Two radiologists independently analyzed two sets of MRI for characterization of GB lesions: a conventional biliary image (CBI) set with T1- and T2WI and a dynamic image; and a DWI set composed of DWI and a CBI set. Diagnostic accuracy and sensitivity were evaluated using the receiver operator characteristic method. The mean apparent diffusion coefficient (ADC) values of the lesions were also calculated.

Results: The Az values were 0.856 and 0.960 for reviewers 1 and 2, respectively, with the CBI set and increased to 0.952 and 0.983 with the DWI set. The mean ADC value of GB carcinoma was 1.46 ± 0.45 × 10(-3) mm(2) /s and that of cholecysititis was 2.16 ± 0.56 × 10(-3) mm(2) /s (P < 0.0001).

Conclusion: Adding DWI to the standard biliary MRI protocol may improve sensitivity for distinguishing GB cancers from benign GB diseases with wall thickening.

Keywords: ADC; MRI; cholecystitis; diffusion-weighted imaging; gallbladder cancer.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adult
  • Aged
  • Aged, 80 and over
  • Cholecystitis / complications*
  • Cholecystitis / pathology*
  • Diffusion Magnetic Resonance Imaging / methods*
  • Female
  • Gallbladder Neoplasms / complications*
  • Gallbladder Neoplasms / pathology*
  • Humans
  • Male
  • Middle Aged
  • Observer Variation
  • Reproducibility of Results
  • Sensitivity and Specificity