Background: Robotic pancreaticoduodenectomy (RPD) is performed for resectable periampullary lesions with comparable outcomes to the open approach.1 Surgical therapy for borderline-resectable (BR) pancreatic tumors is technically challenging and poses a significant risk of bleeding and positive margins.2 As experience with RPD grows at high-volume centers, case selection can be carefully expanded to include complex vascular resections.3 We demonstrate a RPD performed for BR pancreatic adenocarcinoma with portal vein (PV) involvement and presence of anomalous hepatic arterial anatomy.
Methods: A 75-year-old female presented with abdominal pain and obstructive jaundice. She was previously healthy and had a relatively normal body mass index (25.7 kg/m). Endoscopic ultrasound and computed tomography imaging identified a pancreatic head mass measuring 2.3 cm with evidence of concomitant abutment of the PV (90-180 degree) and abutment of a replaced right hepatic artery (rRHA) originating from the superior mesenteric artery (SMA). Following four cycles of neoadjuvant gemcitabine/nab-paclitaxel, restaging imaging demonstrated partial radiographic response, represented by a lesser degree of PV abutment and resolution of rRHA abutment. RPD was performed with side-bite resection of the PV and preservation of rRHA. The video demonstrates the key steps followed in a robotic pancreaticoduodenectomy performed for a technically challenging pancreatic head cancer and highlights robotic control of bleeding from the PV and SMA obviating the need for conversion. Histopathology revealed a residual moderately differentiated ductal adenocarcinoma with 4-of-40 positive lymph nodes and negative surgical margins. The tumor was staged as ypT1cN2 (AJCC 8 edition). The patient had an uneventful postoperative course and was discharged on hospital day 8.
Conclusion: In high-volume centers, the robotic approach can be safely used in selected cases of technically challenging BR pancreatic head cancers.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1007/s11605-021-04937-y | DOI Listing |
Sci Rep
December 2024
KAUST Center of Excellence for Smart Health (KCSH), King Abdullah University of Science and Technology, Thuwal, 23955, Saudi Arabia.
Analyzing microbial samples remains computationally challenging due to their diversity and complexity. The lack of robust de novo protein function prediction methods exacerbates the difficulty in deriving functional insights from these samples. Traditional prediction methods, dependent on homology and sequence similarity, often fail to predict functions for novel proteins and proteins without known homologs.
View Article and Find Full Text PDFSci Rep
December 2024
Department of Production Engineering, KTH Royal Institute of Technology, 11428, Stockholm, Sweden.
This study investigates the implementation of collaborative route planning between trucks and drones within rural logistics to improve distribution efficiency and service quality. The paper commences with an analysis of the unique characteristics and challenges inherent in rural logistics, emphasizing the limitations of traditional methods while highlighting the advantages of integrating truck and drone technologies. It proceeds to review the current state of development for these two technologies and presents case studies that illustrate their application in rural logistics.
View Article and Find Full Text PDFSci Rep
December 2024
Department of Theoretical Electrical Engineering and Diagnostics of Electrical Equipment, Institute of Electrodynamics, National Academy of Sciences of Ukraine, Beresteyskiy, 56, Kyiv-57, 03680, Kyiv, Ukraine.
The integration of Electric Vehicles (EVs) into power grids introduces several critical challenges, such as limited scalability, inefficiencies in real-time demand management, and significant data privacy and security vulnerabilities within centralized architectures. Furthermore, the increasing demand for decentralized systems necessitates robust solutions to handle the growing volume of EVs while ensuring grid stability and optimizing energy utilization. To address these challenges, this paper presents the Demand Response and Load Balancing using Artificial intelligence (DR-LB-AI) framework.
View Article and Find Full Text PDFSci Rep
December 2024
Faculty of Dental Medicine and Oral Health Sciences, McGill University, Montreal, Canada.
Accurate diagnosis of oral lesions, early indicators of oral cancer, is a complex clinical challenge. Recent advances in deep learning have demonstrated potential in supporting clinical decisions. This paper introduces a deep learning model for classifying oral lesions, focusing on accuracy, interpretability, and reducing dataset bias.
View Article and Find Full Text PDFSci Rep
December 2024
Department of Computer Science and Digital Technologies, University of East London, London, UK.
Nursing activity recognition has immense importance in the development of smart healthcare management and is an extremely challenging area of research in human activity recognition. The main reasons are an extreme class-imbalance problem and intra-class variability depending on both the subject and the recipient. In this paper, we apply a unique two-step feature extraction, coupled with an intermediate feature 'Angle' and a new feature called mean min max sum to render the features robust against intra-class variation.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!