Pancreas最新文献

Objective: Exocrine pancreatic insufficiency (EPI) is a common manifestation of chronic pancreatitis (CP) and autoimmune pancreatitis (AIP). This study aimed to estimate the presence of EPI in patients with CP or AIP using alternative clinical markers.

Materials and methods: A machine learning analysis employing a decision tree model was conducted on a retrospective training cohort comprising 57 patients with CP or AIP to identify EPI, defined as fecal elastase-1 levels less than 200 μg/g. The outcomes were then confirmed in a validation cohort of 26 patients.

Results: Thirty-nine patients (68%) exhibited EPI in the training cohort. The decision tree algorithm revealed body mass index (≤21.378 kg/m 2 ) and total protein level (≤7.15 g/dL) as key variables for identifying EPI. The algorithm's performance was assessed using 5-fold cross-validation, yielding area under the receiver operating characteristic curve values of 0.890, 0.875, 0.750, 0.625, and 0.771, respectively. The results from the validation cohort closely replicated those in the training cohort.

Conclusions: Decision tree analysis revealed that EPI in patients with CP or AIP can be identified based on body mass index and total protein. These findings may help guide the implementation of appropriate treatments for EPI.

Objectives: The local renin-angiotensin system promotes angiogenesis and proliferation via vascular endothelial growth factor or epidermal growth factor receptor expression. In this study, we aimed to evaluate the impact of angiotensin system inhibitors (ASIs) on long-term outcomes in patients undergoing surgical resection of pancreatic ductal adenocarcinoma (PDAC).

Methods: A single institutional retrospective analysis was performed using the medical records of patients who underwent pancreatic resection with curative intent for PDAC between January 2005 and December 2018. Patient characteristics and surgical outcomes were compared between patients taking ASIs and those who are not.

Results: A total of 272 patients were included in the study and classified into the ASI group (n = 121) and the non-ASI group (n = 151). The median overall survival times in the ASI group and non-ASI group were 38.0 and 34.0 months ( P = 0.250), and the median recurrence-free survival times were 24.0 and 15.0 months ( P = 0.025), respectively. Multivariate analysis for recurrence-free survival identified the use of ASIs ( P = 0.020), CA19-9 level >500 IU/L ( P = 0.010), positive lymph node metastasis ( P < 0.001), and no adjuvant chemotherapy ( P < 0.001) as independent prognostic factors.

Conclusions: The use of ASI may improve long-term outcomes after surgery for PDAC.

Objectives: Patients with pancreatic disease(s) have a high risk of developing diabetes mellitus (DM). Diabetes mellitus is associated with adverse postoperative outcomes. This study aimed to investigate the prevalence and effects of DM on postoperative outcomes in pancreatic surgery.

Methods: Subgroup analysis of a prospective cohort study conducted at an academic hospital. Patients undergoing pancreatoduodenectomy between January 2019 and November 2022 were included and screened for DM preoperatively using glycated hemoglobin (HbA1c). New-onset DM was diagnosed based on HbA1c ≥ 6.5% (48 mmol/mol). Postoperative outcomes were compared between patients with and without DM.

Results: From 117 patients, 29 (24.8%) were given a diagnosis of DM, and of those, 5 (17.2%) were diagnosed with new-onset DM, and 15 (51.8%) displayed poorly controlled preoperative DM (HbA 1c ≥ 7% [53 mmol/mol]). The incidence of surgical site infections (48.3% vs 27.3% in the non-DM group; P = 0.04) was higher for patients with DM. This association remained significant after adjusting for confounders (odds ratio, 2.60 [95% confidence interval, 1.03-6.66]; P = 0.04).

Conclusions: One-quarter of the patients scheduled for pancreatoduodenectomy had DM; over half of them had poor glycemic control. The association between DM status and surgical site infections revealed in this study emphasizes the importance of adequate preoperative glycemic control.

Objectives: Most patients with intraductal papillary mucinous neoplasms (IPMNs) are diagnosed with a solitary lesion; however, the presence of skip lesions, not appreciable on imaging, has been described. Postoperatively, these missed lesions can continue to grow and potentially become cancerous. Intraoperative pancreatoscopy (IOP) may facilitate detection of such skip lesions in the remnant gland. The aim of this scoping review was to appraise the evidence on the role of IOP in the surgical management of IPMNs.

Materials and methods: Studies reporting on the use of IOP during IPMN surgery were identified through searches of the PubMed, Embase, and Scopus databases. Data extracted included IOP findings, surgical plan modifications, and patient outcomes. The primary outcome of interest was the utility of IOP in surgical decision making.

Results: Ten studies reporting on the use of IOP for IPMNs were identified, representing 147 patients. A total of 46 skip lesions were identified by IOP. Overall, surgical plans were altered in 37% of patients who underwent IOP. No IOP-related complications were reported.

Conclusions: The current literature suggests a potential role of integration of IOP into the management of patients with IPMNs. This tool is safe and feasible and can result in changes in surgical decision making.

Objectives: We aimed to develop and validate a prediction model as the first step in a sequential screening strategy to identify acute pancreatitis (AP) individuals at risk for pancreatic cancer (PC).

Materials and methods: We performed a population-based retrospective cohort study among individuals 40 years or older with a hospitalization for AP in the US Veterans Health Administration. For variable selection, we used least absolute shrinkage and selection operator regression with 10-fold cross-validation to identify a parsimonious logistic regression model for predicting the outcome, PC diagnosed within 2 years after AP. We evaluated model discrimination and calibration.

Results: Among 51,613 eligible study patients with AP, 801 individuals were diagnosed with PC within 2 years. The final model (area under the receiver operating curve, 0.70; 95% confidence interval, 0.67-0.73) included histories of gallstones, pancreatic cyst, alcohol use, smoking, and levels of bilirubin, triglycerides, alkaline phosphatase, aspartate aminotransferase, alanine aminotransferase, and albumin. If the predicted risk threshold was set at 2% over 2 years, 20.3% of the AP population would undergo definitive screening, identifying nearly 50% of PC associated with AP.

Conclusions: We developed a prediction model using widely available clinical factors to identify high-risk patients with PC-associated AP, the first step in a sequential screening strategy.

Background: Pancreatic ductal adenocarcinoma (PDAC) is a deadly disease due to the lack of early detection. Because chronic pancreatitis (CP) patients are a high-risk group for pancreatic cancer, this study aimed to assess the differential miRNA profile in pancreatic tissue of patients with CP and pancreatic cancer.

Methods: MiRNAs were isolated from formalin-fixed paraffin-embedded pancreatic tissue of 22 PDAC patients, 18 CP patients, and 10 normal pancreatic tissues from autopsy (C) cases and processed for next-generation sequencing. Known and novel miRNAs were identified and analyzed for differential miRNA expression, target prediction, and pathway enrichment between groups.

Results: Among the miRNAs identified, 166 known and 17 novel miRNAs were found exclusively in PDAC tissues, while 106 known and 10 novel miRNAs were found specifically in CP tissues. The pathways targeted by PDAC-specific miRNAs and differentially expressed miRNAs between PDAC versus CP tissues and PDAC versus control tissues were the proteoglycans pathway, Hippo signaling pathway, adherens junction, and transforming growth factor-β signaling pathway.

Conclusions: This study resulted in a set of exclusive and differentially expressed miRNAs in PDAC and CP can be assessed for their diagnostic value. In addition, studying the role of miRNA-target gene interactions in carcinogenesis may open new therapeutic avenues.

Abstract: Pancreatic ductal adenocarcinoma (PDAC) stands as one of the most lethal diseases globally, boasting a grim 5-year survival prognosis. The origin cell and the molecular signaling pathways that drive PDAC progression are not entirely understood. This review comprehensively outlines the categorization of PDAC and its precursor lesions, expounds on the creation and utility of genetically engineered mouse models used in PDAC research, compiles a roster of commonly used markers for pancreatic progenitors, duct cells, and acinar cells, and briefly addresses the mechanisms involved in the progression of PDAC. We acknowledge the value of precise markers and suitable tracing tools to discern the cell of origin, as it can facilitate the creation of more effective models for PDAC exploration. These conclusions shed light on our existing understanding of foundational genetically engineered mouse models and focus on the origin and development of PDAC.

Objectives: Pancreatic ductal adenocarcinoma is an intractable disease with frequent recurrence after resection and adjuvant therapy. The present study aimed to clarify whether artificial intelligence-assisted analysis of histopathological images can predict recurrence in patients with pancreatic ductal adenocarcinoma who underwent resection and adjuvant chemotherapy with tegafur/5-chloro-2,4-dihydroxypyridine/potassium oxonate.

Materials and methods: Eighty-nine patients were enrolled in the study. Machine-learning algorithms were applied to 10-billion-scale pixel data of whole-slide histopathological images to generate key features using multiple deep autoencoders. Areas under the curve were calculated from receiver operating characteristic curves using a support vector machine with key features alone and by combining with clinical data (age and carbohydrate antigen 19-9 and carcinoembryonic antigen levels) for predicting recurrence. Supervised learning with pathological annotations was conducted to determine the significant features for predicting recurrence.

Results: Areas under the curves obtained were 0.73 (95% confidence interval, 0.59-0.87) by the histopathological data analysis and 0.84 (95% confidence interval, 0.73-0.94) by the combinatorial analysis of histopathological data and clinical data. Supervised learning model demonstrated that poor tumor differentiation was significantly associated with recurrence.

Conclusions: Results indicate that machine learning with the integration of artificial intelligence-driven evaluation of histopathological images and conventional clinical data provides relevant prognostic information for patients with pancreatic ductal adenocarcinoma.