Accuracy of visual analysis vs. apparent diffusion coefficient quantification in differentiating solid benign and malignant focal liver lesions with diffusion-weighted imaging

Radiol Med. 2013 Apr;118(3):343-55. doi: 10.1007/s11547-012-0873-z. Epub 2012 Sep 17.

Abstract

Purpose: The authors compared the accuracy of diffusion-weighted imaging (DWI) visual analysis (VA) vs. apparent diffusion coefficient quantification (ADC-Q) in assessing malignancy of solid focal liver lesions (FLLs).

Materials and methods: Using a 1.5-T system, two radiologists retrospectively assessed as benign or malignant 50 solid FLLs: (a) by VA of signal intensity on DWI images at b=800 s/mm(2) and ADC map; (b) by quantifying lesion ADC. Reference standard included histology or follow-up confirmation of diagnosis by a consensus panel. Receiver operating characteristic (ROC) curve analysis was performed.

Results: because of 20 false-negative hepatocellular carcinomas, VA showed lower accuracy than ADC-Q (52.0% VS. 68.0%). however, stratified accuracy for metastases was higher with VA (75.0 VS. 66%). ADC and signal features of malignant and benign FLLs were found to largely overlap.

Conclusions: VA performed worse than ADC-Q for hepatocellular carcinoma and better for metastases. Overall, the accuracy of both methods was limited because of the overlap in visual appearance and ADC values between solid benign and malignant FLLs.

Publication types

  • Comparative Study

MeSH terms

  • Adenoma / diagnosis
  • Adenoma / pathology
  • Adult
  • Aged
  • Carcinoma, Hepatocellular / diagnosis
  • Carcinoma, Hepatocellular / pathology
  • Diagnosis, Differential
  • Diffusion Magnetic Resonance Imaging / methods*
  • False Negative Reactions
  • Female
  • Focal Nodular Hyperplasia / diagnosis
  • Focal Nodular Hyperplasia / pathology
  • Hemangioma / diagnosis
  • Hemangioma / pathology
  • Humans
  • Image Interpretation, Computer-Assisted
  • Liver Neoplasms / diagnosis*
  • Liver Neoplasms / pathology
  • Male
  • Middle Aged
  • ROC Curve
  • Regression Analysis
  • Retrospective Studies
  • Sensitivity and Specificity
  • Statistics, Nonparametric