Correlation of apparent diffusion coefficient measured by diffusion-weighted MRI and clinicopathologic features in pancreatic cancer patients

J Hepatobiliary Pancreat Sci. 2013 Feb;20(2):243-8. doi: 10.1007/s00534-011-0491-5.

Abstract

Background/purpose: The aim of this study was to evaluate the clinical usefulness of diffusion-weighted magnetic resonance imaging (DWI) in patients with pancreatic cancer by comparing the apparent diffusion coefficient (ADC) value with clinicopathologic features.

Methods: Twenty-two consecutive patients (12 men, 10 women; mean age 64.4 years) with pancreatic cancer underwent DWI before surgery. We retrospectively investigated the correlations between tumor ADC value and clinicopathologic features.

Results: Apparent diffusion coefficient value was significantly lower for pancreatic cancer than for noncancerous tissue (P < 0.001). Receiver operating characteristic analysis yielded an optimal ADC cutoff value of 1.21 × 10(-3) mm(2)/s to distinguish pancreatic cancer from noncancerous tissue. There was a significant negative correlation between ADC value and tumor size (r = -0.59, P = 0.004) and between ADC value and number of metastatic lymph nodes (r = -0.56, P = 0.007). Tumors with low ADC value had a significant tendency to show high portal venous system invasion (P = 0.02) and extrapancreatic nerve plexus invasion (P = 0.01).

Conclusions: Apparent diffusion coefficient value appears to be a promising parameter for detecting pancreatic cancer and evaluating the degree of malignancy of pancreatic cancer.

Publication types

  • Comparative Study

MeSH terms

  • Adult
  • Aged
  • Diagnosis, Differential
  • Diffusion Magnetic Resonance Imaging / methods*
  • Female
  • Follow-Up Studies
  • Humans
  • Male
  • Middle Aged
  • Neoplasm Staging / methods*
  • Pancreas / pathology*
  • Pancreatectomy*
  • Pancreatic Neoplasms / diagnosis*
  • Pancreatic Neoplasms / surgery
  • Preoperative Period
  • Reproducibility of Results
  • Retrospective Studies
  • Severity of Illness Index