Outcomes after Transgastric Drainage of Pancreatic Duct Leaks.

PURPOSE: To determine the outcomes of transgastric drainage (TGD) of pancreatic duct leaks (PDLs), including fluid collections and pancreaticocutaneous fistulae (PCFs). MATERIALS AND METHODS: Fifty-four patients who underwent attempted TGD of a PDL from 1992 to 2020 were identified. Data regarding patient comorbidities, fluid collection characteristics, technical success, drain exchanges and removals, recurrent collections, and complications were analyzed. RESULTS: Forty-one patients (41/54, 76%) had a history of pancreatitis. Sixteen patients (16/54, 30%) had a history of recent abdominal surgery. Peripancreatic fluid collections were 11.2 cm ± 4.6 in greatest dimension prior to drainage. Twenty-one collections (21/54, 39%) demonstrated biochemical and/or imaging evidence of an active communication to the pancreatic duct, and 16 (16/54, 30%) of these patients had a PCF due to a direct percutaneous drain prior to TGD. TGD was technically successful in 53 patients (53/54, 98%). During the follow-up period, 46 patients (46/53, 87%) were able to undergo drain removal after resolution of the fluid collection, with a mean catheter indwelling time of 3 months and a median of 1 catheter exchange. There were 2 severe (2/53, 4%) and 4 moderate (4/53, 8%) complications, the most common of which was drain dislodgement requiring repeat transgastric puncture. Recurrent fluid collections were observed in 8 patients (8/53, 15%) after a mean of 5 months following drain removal. There were no recurrent PCFs. CONCLUSIONS: TGD of PDLs is technically feasible and efficacious in the vast majority of patients with a relatively low complication rate. This technique is effective in preventing or treating the long-term debilitating complication of PCF.

Single-Step Method for Pull-Type Gastrostomy Tube Placement.

Single-step pull-type gastrostomy tube (PGT) placement is a method involving gastric puncture with a curved 18-gauge trocar needle allowing retrograde cannulation of the gastroesophageal junction without use of a sheath or snare. This retrospective review of 102 patients who underwent single-step PGT placement demonstrated 91% success in advancing the wire up the esophagus using only the curved trocar. Successful placement of a gastrostomy tube was 100%. Two major and 2 minor complications occurred within 30 days, all unrelated to the single-step technique. Mean fluoroscopy time for all patients was 5.1 min (range, 1.5-19.2 min). Single-step PGT placement is an effective, safe, fast, and equipment-sparing method for gastrostomy placement.

Bayesian Networks in Radiology.

A Bayesian network is a graphical model that uses probability theory to represent relationships among its variables. The model is a directed acyclic graph whose nodes represent variables, such as the presence of a disease or an imaging finding. Connections between nodes express causal influences between variables as probability values. Bayesian networks can learn their structure (nodes and connections) and/or conditional probability values from data. Bayesian networks offer several advantages: (a) they can efficiently perform complex inferences, (b) reason from cause to effect or vice versa, (c) assess counterfactual data, (d) integrate observations with canonical ("textbook") knowledge, and (e) explain their reasoning. Bayesian networks have been employed in a wide variety of applications in radiology, including diagnosis and treatment planning. Unlike deep learning approaches, Bayesian networks have not been applied to computer vision. However, hybrid artificial intelligence systems have combined deep learning models with Bayesian networks, where the deep learning model identifies findings in medical images and the Bayesian network formulates and explains a diagnosis from those findings. One can apply a Bayesian network's probabilistic knowledge to integrate clinical and imaging findings to support diagnosis, treatment planning, and clinical decision-making. This article reviews the fundamental principles of Bayesian networks and summarizes their applications in radiology. Keywords: Bayesian Network, Machine Learning, Abdominal Imaging, Musculoskeletal Imaging, Breast Imaging, Neurologic Imaging, Radiology Education Supplemental material is available for this article. © RSNA, 2023.

A Chemical and Enzymatic Approach to Study Site-Specific Sumoylation.

A variety of cellular pathways are regulated by protein modifications with ubiquitin-family proteins. SUMO, the Small Ubiquitin-like MOdifier, is covalently attached to lysine on target proteins via a cascade reaction catalyzed by E1, E2, and E3 enzymes. A major barrier to understanding the diverse regulatory roles of SUMO has been a lack of suitable methods to identify protein sumoylation sites. Here we developed a mass-spectrometry (MS) based approach combining chemical and enzymatic modifications to identify sumoylation sites. We applied this method to analyze the auto-sumoylation of the E1 enzyme in vitro and compared it to the GG-remnant method using Smt3-I96R as a substrate. We further examined the effect of smt3-I96R mutation in vivo and performed a proteome-wide analysis of protein sumoylation sites in Saccharomyces cerevisiae. To validate these findings, we confirmed several sumoylation sites of Aos1 and Uba2 in vivo. Together, these results demonstrate that our chemical and enzymatic method for identifying protein sumoylation sites provides a useful tool and that a combination of methods allows a detailed analysis of protein sumoylation sites.