• Contrast-enhanced endoscopic ultrasound
• Elastometry
• Endoscopic ultrasound
• Pancreatic cancer
• Pancreatitis

• cytoreductive surgery
• foamy glands
• hyperthermic intraperitoneal chemotherapy
• large duct variant
• pancreatic ductal adenocarcinoma

• K23 MD017217 NIMHD NIH HHS

• Endoscopic ultrasound-guided fine needle aspiration
• Korea
• Pancreatic neoplasms
• Survey

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.

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.

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.

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.

• EUS
• autoimmune pancreatitis
• pancreatic cancer
• prediction model

• EUS-FNB
• Franseen needle
• histopathological assessment
• small pancreatic lesions

• Core tissue
• Endosonography
• Pancreatic neoplasm
• Tissue sampling

• endoscopic ultrasound
• fine needle aspiration
• fine needle biopsy
• meta-analysis
• pancreatic masses

• Biopsy, Fine-Needle/adverse effects
• Biopsy, Fine-Needle/methods
• Biopsy, Fine-Needle/standards
• Endoscopic Ultrasound-Guided Fine Needle Aspiration/adverse effects
• Endoscopic Ultrasound-Guided Fine Needle Aspiration/methods
• Endoscopic Ultrasound-Guided Fine Needle Aspiration/standards
• Humans
• Pancreatic Diseases/pathology
• Randomized Controlled Trials as Topic
• Sensitivity and Specificity
• Specimen Handling

To compare the diagnostic yield and safety of 22G endoscopic ultrasound-guided fine-needle aspiration (EUS-FNA) and endoscopic ultrasound-guided fine-needle biopsy (EUS-FNB) in the diagnosis of pancreatic solid lesions.

Between April 2014 and September 2015, 36 patients with pancreatic solid lesions were included for endoscopic ultrasound test. Patients were randomly divided into two groups: EUS-FNA (n = 18) and EUS-FNB (n = 18). Each nidus was punctured three times (15 ~ 20 insertions for each puncture) with a 22G needle. The core specimens were analyzed, and the diagnostic yields of FNA and FNB were evaluated.

The procedure success rate was 100% with no complications. Cytological and histological examinations found that the diagnostic yield of FNB and FNA were both 83.3%. To get a definitive diagnosis, FNB needed fewer punctures than FNA (1.11 vs. 1.83; P  <  0.05).

22G EUS-FNB is a safe and effective way to diagnose pancreatic solid lesions. FNB required a lower number of needle passes to achieve a diagnosis compared with FNA.

• Pancreatic solid lesion
• Ultrasound-guided fine-needle aspiration
• Ultrasound-guided fine-needle biopsy

• Cone-beam computed tomography
• Dose fractionation
• Models, animal
• Pancreatic neoplasms
• Radiotherapy, image-guided

• Biopsy
• diagnosis
• endosonography
• fine needle aspiration
• histopathology
• needle type
• pancreatic cancer
• ultrasound guided biopsy

Endoscopic ultrasound (EUS) is an important part of modern gastrointestinal endoscopy and now has an integral role in the diagnostic evaluation of pancreatic diseases. Furthermore, as EUS technology has advanced, it has increasingly become a therapeutic procedure, and the prospect of multiple applications of interventional EUS for the pancreas is truly on the near horizon. However, this review focuses on the established diagnostic and therapeutic roles of EUS that are used in current clinical practice. In particular, the diagnostic evaluation of acute pancreatitis, chronic pancreatitis, cystic pancreatic lesions and solid masses of the pancreas are discussed. The newer enhanced imaging modalities of elastography and contrast enhancement are evaluated in this context. The main therapeutic aspects of pancreatic EUS are then considered, namely celiac plexus block and celiac plexus neurolysis for pain control in chronic pancreatitis and pancreas cancer, and EUS-guided drainage of pancreatic fluid collections.

• Endoscopic ultrasonography
• Fine needle aspiration
• Pancreatic cyst
• Pancreatic neoplasms
• Pancreatitis

• Drainage/methods
• Endosonography
• Humans
• Nerve Block/methods
• Pancreatic Diseases/diagnostic imaging
• Pancreatic Diseases/therapy
• Predictive Value of Tests
• Treatment Outcome
• Ultrasonography, Interventional

• Molecular imaging
• PDAC
• Ultrasonography

• Adenocarcinoma/diagnosis
• Adenocarcinoma/therapy
• Animals
• Carcinoma, Pancreatic Ductal/diagnosis
• Carcinoma, Pancreatic Ductal/therapy
• Diagnostic Imaging/methods
• Early Diagnosis
• Humans
• Mice
• Pancreatic Neoplasms/diagnosis
• Pancreatic Neoplasms/therapy
• Reproducibility of Results
• Sensitivity and Specificity

• 16464 Cancer Research UK
• R01 CA075059 NCI NIH HHS
• R37 CA075059 NCI NIH HHS
• R37-CA-075059 NCI NIH HHS

Differentiating pancreatic cancer (PC) from normal tissue by computer-aided diagnosis of EUS images were quite useful. The current study was designed to investigate the feasibility of using computer-aided diagnostic (CAD) techniques to extract EUS image parameters for the differential diagnosis of PC and chronic pancreatitis (CP).

This study recruited 262 patients with PC and 126 patients with CP. Typical EUS images were selected from the sample sets. Texture features were extracted from the region of interest using computer-based techniques. Then the distance between class algorithm and sequential forward selection (SFS) algorithm were used for a better combination of features; and, later, a support vector machine (SVM) predictive model was built, trained, and validated. Overall, 105 features of 9 categories were extracted from the EUS images for pattern classification. Of these features, the 16 were selected as a better combination of features. Then, SVM predictive model was built and trained. The total cases were randomly divided into a training set and a testing set. The training set was used to train the SVM, and the testing set was used to evaluate the performance of the SVM. After 200 trials of randomised experiments, the average accuracy, sensitivity, specificity, the positive and negative predictive values of pancreatic cancer were 94.2±0.1749%,96.25±0.4460%, 93.38±0.2076%, 92.21±0.4249% and 96.68±0.1471%, respectively.

Digital image processing and computer-aided EUS image differentiation technologies are highly accurate and non-invasive. This technology provides a kind of new and valuable diagnostic tool for the clinical determination of PC.