Differentiation of mass-forming focal pancreatitis from pancreatic ductal adenocarcinoma: value of characterizing dynamic enhancement patterns on contrast-enhanced MR images by adding signal intensity color mapping

作者：Kim Mimi; Jang Kyung Mi^*; Kim Jae Hun; Jeong Woo Kyoung; Kim Seong Hyun; Kang Tae Wook; Kim Young Kon; Cha Dong Ik; Kim Kyunga

来源：European Radiology, 2017, 27(4): 1722-1732.

DOI：10.1007/s00330-016-4522-0

• Pancreatic ductal adenocarcinomas
• Mass-forming focal pancreatitis
• Magnetic resonance imaging
• Signal intensity colour mapping
• Classification tree analysis

To evaluate the value of dynamic enhancement patterns on contrast-enhanced MR images by adding signal intensity colour mapping (SICM) to differentiate mass-forming focal pancreatitis (MFFP) from pancreatic ductal adenocarcinoma (PDAC).
Forty-one clinicopathologically proven MFFPs and 144 surgically confirmed PDACs were enrolled. Laboratory and MR imaging parameters were used to differentiate MFFP from PDAC. In particular, enhancement patterns on MR images adding SICM were evaluated. By using classification tree analysis (CTA), we determined the predictors for the differentiation of MFFP from PDAC.
In the CTA, with all parameters except enhancement pattern on SICM images, ductal obstruction grade and T1 hypointensity grade of the pancreatic lesion were the first and second splitting predictor for differentiation of MFFP from PDAC, in order. By adding an enhancement pattern on the SICM images to CTA, the enhancement pattern was the only splitting predictor to differentiate MFFP from PDAC. The CTA model including enhancement pattern on SICM images has sensitivity of 78.0 %, specificity of 99.3 %, and accuracy of 94.6 % for differentiating MFFP from PDAC.
The characterization of enhancement pattern for pancreatic lesions on contrast-enhanced MR images adding SICM would be helpful to differentiate MFFP from PDAC.
SICM was useful to characterize enhancement pattern.
Enhancement pattern on SICM was the only splitting predictor on CTA.
This model may be useful for differentiating MFFP from PDAC.

• 出版日期2017-4