Objective: To use the concept of benchmarking to establish robust and standardized outcome references after pancreatico-duodenectomy (PD). Background: Best achievable results after PD are unknown. Consequently, outcome comparisons among different cohorts, centers or with novel surgical techniques remain speculative. Methods: This multicenter study analyzes consecutive patients (2012–2015) undergoing PD in 23 international expert centers in pancreas surgery. Outcomes in patients without significant comorbidities and major vascular resection (benchmark cases) were analyzed to establish 20 outcome benchmarks for PD. These benchmarks were tested in a cohort with a poorer preoperative physical status (ASA class ≥3) and a cohort treated by minimally invasive approaches. Results: Two thousand three hundred seventy-five (38%) low-risk cases out of a total of 6186 PDs were analyzed, disclosing low in-hospital mortality (≤1.6%) but high morbidity, with a 73% benchmark morbidity rate cumulated within 6 months following surgery. Benchmark cutoffs for pancreatic fistulas (B-C), severe complications (≥ grade 3), and failure-to-rescue rate were 19%, 30%, and 9%, respectively. The ASA ≥3 cohort showed comparable morbidity but a higher in hospital-mortality (3% vs 1.6%) and failure-to-rescue rate (16% vs 9%) than the benchmarks. The proportion of benchmark cases performed varied greatly across centers and continents for both open (9%–93%) and minimally invasive (11%–62%) PD. Centers operating mostly on complex PD cases disclosed better results than those with a majority of low-risk cases. Conclusion: The proposed outcome benchmarks for PD, established in a large-scale international patient cohort and tested in 2 different cohorts, may allow for meaningful comparisons between different patient cohorts, centers, countries, and surgical techniques.

Pancreatic cancer is a devastating disease that has a grim prognosis, highlighting the need for improved screening, diagnosis, and treatment strategies. Currently, the sole biomarker for pancreatic ductal adenocarcinoma (PDAC) authorized by the U.S. Food and Drug Administration is CA 19-9, which proves to be the most beneficial in tracking treatment response rather than in early detection. In recent years, proteomics has emerged as a powerful tool for advancing our understanding of pancreatic cancer biology and identifying potential biomarkers and therapeutic targets. This review aims to offer a comprehensive survey of proteomics’ current status in pancreatic cancer research, specifically accentuating its applications and its potential to drastically enhance screening, diagnosis, and treatment response. With respect to screening and diagnostic precision, proteomics carries the capacity to augment the sensitivity and specificity of extant screening and diagnostic methodologies. Nonetheless, more research is imperative for validating potential biomarkers and establishing standard procedures for sample preparation and data analysis. Furthermore, proteomics presents opportunities for unveiling new biomarkers and therapeutic targets, as well as fostering the development of personalized treatment strategies based on protein expression patterns associated with treatment response. In conclusion, proteomics holds great promise for advancing our understanding of pancreatic cancer biology and improving patient outcomes. It is essential to maintain momentum in investment and innovation in this arena to unearth more groundbreaking discoveries and transmute them into practical diagnostic and therapeutic strategies in the clinical context.

Introduction: Pancreas transplantation is currently the only treatment that can re-establish normal endocrine pancreatic function. Despite all efforts, pancreas allograft survival and rejection remain major clinical problems. The purpose of this study was to identify features that could signal patients at risk of pancreas allograft rejection. Methods: We collected 74 features from 79 patients who underwent simultaneous pancreas–kidney transplantation (SPK) and used two widely-applicable classification methods, the Naive Bayesian Classifier and Support Vector Machine, to build predictive models. We used the area under the receiver operating characteristic curve and classification accuracy to evaluate the predictive performance via leave-one-out cross-validation. Results: Rejection events were identified in 13 SPK patients (17.8%). In feature selection approach, it was possible to identify 10 features, namely: previous treatment for diabetes mellitus with long-term Insulin (U/I/day), type of dialysis (peritoneal dialysis, hemodialysis, or pre-emptive), de novo DSA, vPRA_Pre-Transplant (%), donor blood glucose, pancreas donor risk index (pDRI), recipient height, dialysis time (days), warm ischemia (minutes), recipient of intensive care (days). The results showed that the Naive Bayes and Support Vector Machine classifiers prediction performed very well, with an AUROC and classification accuracy of 0.97 and 0.87, respectively, in the first model and 0.96 and 0.94 in the second model. Conclusion: Our results indicated that it is feasible to develop successful classifiers for the prediction of graft rejection. The Naive Bayesian generated nomogram can be used for rejection probability prediction, thus supporting clinical decision making.