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

• Published in: Endoscopic Ultrasound Vol. 10; no. 4; pp. 280 - 287
• Main Authors: Guo, Tao, Xu, Tao, Zhang, Shengyu, Lai, Yamin, Wu, Xi, Wu, Dongsheng, Feng, Yunlu, Jiang, Qingwei, Wang, Qiang, Qian, Jiaming, Yang, Aiming
• Format: Journal Article
• Language: English
• Published: India Wolters Kluwer India Pvt. Ltd 01.07.2021; Medknow Publications and Media Pvt. Ltd; Wolters Kluwer - Medknow
• Subjects: Medical research; Medicine, Experimental; Original; Pancreatic cancer; Pancreatitis; autoimmune pancreatitis; EUS; pancreatic cancer; prediction model
• ISSN: 2303-9027; 2226-7190
• DOI: 10.4103/EUS-D-20-00212

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.