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ORIGINAL ARTICLE
Year : 2021  |  Volume : 10  |  Issue : 4  |  Page : 280-287

The role of EUS in diagnosing focal autoimmune pancreatitis and differentiating it from pancreatic cancer


1 Department of Gastroenterology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, China
2 Depatment of Epidemiology and Biostatistics, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences and School of Basic Medicine, Peking Union Medical College, Beijing, China

Correspondence Address:
Dr. Aiming Yang
Department of Gastroenterology, Peking Union Medical College Hospital, No. 1 Shuaifuyuan, Dong Cheng District, Beijing 100730
China
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/EUS-D-20-00212

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Background and Objectives: The clinical presentation of focal autoimmune pancreatitis (FAIP) and together with radiological overlap can mimic pancreatic cancer (PC). The aim of this study is to construct and validate a prediction model for differentiating FAIP from PC according to EUS characteristics. Patients and Methods: Ninety patients with FAIP and 196 patients with PC, who consecutively underwent EUS at our center from January 2013 to December 2018, were retrospectively included in the study. The enrolled patients were randomly divided into either a derivation sample or a validation sample. According to EUS characteristics, multivariate stepwise logistic regression and receiver operating characteristics (ROC) analyses were used to construct a prediction model in derivation sample, and then, the efficacy was assessed in validation sample. Results: EUS characteristics that were suggestive of FAIP included diffuse hypoechogenicity, hyperechoic foci/stands or lobularity (parenchymal heterogeneity), bile duct wall thickening and peripancreatic hypoechoic margin; and EUS features favoring PC included focal hypoechogenicity, absence of parenchymal heterogeneity, pancreatic duct dilation, and vessel involvement. The prediction model, with an area under the ROC curve of more than 0.95, had a good capability to distinguish FAIP from PC. By using the optimal cutoff value, the efficacy of model for diagnosing PC showed 83.7%–91.8% sensitivity and 93.3%–95.6% specificity. Conclusions: It is feasible to differentiate FAIP from PC based on EUS characteristics. The prediction model built in this study needs to be further confirmed by multicenter prospective researches.


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