Clinical impact of pancreatic steatosis measured by CT on the risk of post-ERCP pancreatitis: a multicenter prospective trial.

BACKGROUND AND AIMS: Pancreatic steatosis (PS) may be a risk factor for acute pancreatitis. Whether it is also a risk factor for post-ERCP pancreatitis (PEP) has not been evaluated. This study aimed to determine the impact of PS on PEP development. METHODS: This multicenter prospective trial enrolled 786 consecutive patients who underwent contrast-enhanced abdominal CT and subsequent first-time ERCP. PS was evaluated based on pancreatic attenuation on unenhanced CT images. The risk of PS for the development of PEP was evaluated using a logistic regression model. RESULTS: Of 527 patients included in the study, 157 (29.8%) had PS and 370 (70.2%) did not. At 24 hours after ERCP, there was a significant difference in the PEP identified in 22 patients (14.0%) in the PS group and 23 patients (6.2%) in the "no PS" (NPS) group (P = .017). Diabetes and hypertension were more common in the PS group than in the NPS group; no differences in dyslipidemia were found. Patients with PS had a higher risk for the development of PEP than those with NPS (odds ratio, 2.09; 95% confidence interval, 1.08-4.03). No other variables were identified as risk factors for PEP. CONCLUSIONS: PS is a significant risk factor for PEP for which preventive measures should be considered. Standardized measurement protocols to assess PS by CT are needed. (Clinical trial registration number: KCT0006068.).

Impact of pancreatic fat on the risk of post-endoscopic retrograde cholangiopancreatography pancreatitis.

BACKGROUND AND STUDY AIMS: Fatty pancreas is a potential risk factor for acute pancreatitis; however, whether it is also a risk factor for post-endoscopic retrograde cholangiopancreatography pancreatitis (PEP) has not been evaluated. We aimed to determine the impact of fatty pancreas on the development of PEP. METHODS: We analyzed the data of patients who underwent abdominal computed tomography (CT) scan and sequential therapeutic endoscopic retrograde cholangiopancreatography (ERCP). Fatty change in the pancreas was evaluated based on pancreatic attenuation of unenhanced image on CT scan. The risk of fatty pancreas for the development of PEP was evaluated using a logistic regression model. RESULTS: Of a total of 858 patients included in the study, 354 (41.3%) had fatty pancreas, while 504 (58.7%) did not have fatty pancreas. PEP developed in 28 patients (7.9%) in the fatty pancreas group and in 13 patients (2.6%) in the no fatty pancreas group. Fatty pancreas was significantly associated with the development of PEP (odds ratio [OR] [95% confidence interval [CI]] 2.38 [1.16-4.87]). A history of acute pancreatitis, female sex, difficult cannulation, and endoscopic papillary balloon dilation were also risk factors for the development of PEP. The risk for moderate-to-severe PEP development tended to be higher in the fatty pancreas group than in the no fatty pancreas group (OR [95% CI] 5.61 [0.63-49.62]). CONCLUSIONS: Fatty pancreas is a significant risk factor for the development of PEP. Clinicians need to be aware of the risk of fatty pancreas for the development of PEP prior to performing ERCP.

Efficacy of an artificial neural network algorithm based on thick-slab magnetic resonance cholangiopancreatography images for the automated diagnosis of common bile duct stones.

BACKGROUND AND AIM: Magnetic resonance cholangiopancreatography (MRCP) can accurately diagnose common bile duct (CBD) stones but is laborious to interpret. We developed an artificial neural network (ANN) capable of automatically assisting physicians with the diagnosis of CBD stones. This study aimed to evaluate the ANN's diagnostic performance for detecting CBD stones in thick-slab MRCP images and identify clinical factors predictive of accurate diagnosis. METHODS: The presence of CBD stones was confirmed via direct visualization through endoscopic retrograde cholangiopancreatography (ERCP). The absence of CBD stones was confirmed by either a negative endoscopic ultrasound accompanied by clinical improvements or negative findings on ERCP. Our base networks were constructed using state-of-the-art EfficientNet-B5 neural network models, which are widely used for image classification. RESULTS: In total, 3156 images were collected from 789 patients. Of these, 2628 images from 657 patients were used for training. An additional 1924 images from 481 patients were prospectively collected for validation. Across the entire prospective validation cohort, the ANN achieved a sensitivity, specificity, positive predictive value, negative predictive value, and overall accuracy of 93.03%, 97.05%, 97.01%, 93.12%, and 95.01%, respectively. Similarly, a radiologist achieved a sensitivity, specificity, positive predictive value, negative predictive value, and overall accuracy 91.16%, 93.25%, 93.22%, 90.20%, and 91.68%, respectively. In multivariate analysis, only bile duct diameter > 10 mm (odds ratio = 2.45, 95% confidence interval [1.08-6.07], P = 0.040) was related to ANN diagnostic accuracy. CONCLUSION: Our ANN algorithm automatically and quickly diagnoses CBD stones in thick-slab MRCP images, therein aiding physicians with optimizing clinical practice, such as whether to perform ERCP.