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
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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.
KeywordsPancreatic ductal adenocarcinomas Mass-forming focal pancreatitis Magnetic resonance imaging Signal intensity colour mapping Classification tree analysis
Pancreatic ductal adenocarcinomas
Mass-forming focal pancreatitis
Common bile duct
Main pancreatic duct
Signal intensity colour mapping
Apparent diffusion coefficient
Classification tree analysis
The scientific guarantor of this publication is Won Jae Lee, the head of radiology department. The authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article. The authors state that this work has not received any funding. One of the authors has significant statistical expertise, Kyunga Kim. Institutional review board approval was obtained. Written informed consent was waived by the Institutional review board. No study subjects or cohorts have been previously reported
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