Clinical Outcomes of Robotic Resection for Perihilar Cholangiocarcinoma: A First, Multicenter, Trans-Atlantic, Expert-Center, Collaborative Study.

INTRODUCTION: Perihilar cholangiocarcinoma is a difficult cancer to treat with frequent vascular invasion, local recurrence, and poor survival. Due to the need for biliary anastomosis and potential vascular resection, the standard approach is an open operation. Suboptimal outcomes after laparoscopic resection had been sporadically reported by high-volume centers. In this first, Trans-Atlantic, multicenter study, we report our outcomes of robotic resection for perihilar cholangiocarcinoma. This is the largest study of its kind in the Western hemisphere. METHODS: Between 2016 and 2023, we prospectively followed patients undergoing robotic resection for perihilar cholangiocarcinoma at three, high-volume, robotic, liver-surgery centers. RESULTS: Thirty-eight patients underwent perihilar cholangiocarcinoma utilizing the robotic technique; Klatskin type-3 was the most common. The median age was 72 years, and 82% of the patients underwent preoperative biliary drainage. Median operative time was 481 minutes with a median estimated blood loss of 200 mL. The number of harvested lymph nodes was seven, and 11 (28%) patients yielded positive lymph nodes. Three patients required vascular reconstruction; 18% of patients had >1 biliary anastomosis. R0 resection margins were achieved in 82% of patients. Clavien-Dindo Grade ≥3 complications were seen in 16% of patients. The length of stay was 6 days. Five patients had an unplanned readmission within 30 days. One patient died within 30 days. With a median follow-up of 15 months, 68% of patients are alive without disease, 13% recurred, and 19% died. CONCLUSIONS: Application of the robotic platform for perihilar cholangiocarcinoma is safe and feasible with acceptable short-term clinical and oncological outcomes.

Pancreas Rejection in the Artificial Intelligence Era: New Tool for Signal Patients at Risk.

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.