Predictive modeling for metastasis in oncology has gained significant traction due to its potential to improve prognosis, guide treatment strategies and enhance patient outcomes. Current methods leverage advancements in machine learning, genomics and imaging technologies to predict the likelihood of cancer spread. Techniques such as logistic regression, decision trees, support vector machines and neural networks have been employed to analyze clinical, pathological, and molecular data. Genomic profiling, liquid biopsies, and radiomics are increasingly integrated into these models to identify metastatic patterns and risk factors. Despite these advances, challenges persist, including data heterogeneity, model interpretability, and the need for larger, high-quality datasets for validation. Furthermore, the integration of artificial intelligence with precision medicine offers promising avenues for more personalized metastasis predictions. Future directions focus on enhancing model accuracy through deep learning, improving the interpretability of black-box models, and incorporating multi-omics data to capture the complexity of metastatic mechanisms. With the advent of advanced computational tools and growing datasets, predictive modeling in oncology is poised to revolutionize metastasis management, offering clinicians’ valuable insights for early detection and tailored treatment strategies.

Aim: This study aimed to assess and compare outcomes of robotic inguinal hernia repair (RIHR) in patients under and over 70 years old, performed by a fellowship-trained robotic surgeon at a single institution. Methods: A retrospective analysis of patients undergoing robotic primary transabdominal preperitoneal inguinal hernia repair between 2020 and 2022 was conducted. Patients were categorized into two age groups: those under 70 years and 70 years and older. Data were collected through chart reviews with a mean follow-up of 30 days. Concurrently, a systematic review (SR) of relevant high-level literature was carried out. Results: Among the 37 patients studied, 75.7% (n = 28) were male, with a mean age of 64.8 years. Demographic features did not significantly differ based on age groups. Patients > 70 years had a higher incidence of reported complications (52.3% vs. 87.5%, p < 0.461). There were no differences in operative time or length of stay between the groups. In the SR, only 23.7% (n = 9) of studies provided age-related conclusions. Three studies identified age over 70 as a risk factor for postoperative complications, while two studies suggested that RIHR is feasible and safe in patients aged 80 years and older. Conclusion: Patients over 70 years old demonstrated a higher incidence of complications compared to younger patients. However, current literature indicates that the robotic approach may offer a safe and minimally invasive option for inguinal hernia repair in both younger and older adults.

Background: Over the years, surgical techniques have evolved, resulting in an abundance of available procedures in the armamentarium of metabolic and bariatric surgeons, and the technology has also advanced in a similar way. Significant steps have been made in stapling technology especially, introducing artificial intelligence (AI) in optimizing this technology for better treatment outcomes. The introduction of AI in stapling technology showed a decrease in potential stapling complications not only in MBS, but also in other (surgical) specialties. Areas Covered: This review will cover the general principles of stapling in surgery, but with an emphasis on both the technical and anatomical considerations. We will also discuss the mechanisms of staplers and potential safety hazards. Finally, we will focus on how AI is integrated in stapling technology, potential pros and cons, and areas for future development of stapling technology and the integration of AI. Conclusions: In metabolic and bariatric surgery, stapling is a technical procedure that requires a comprehensive understanding of the anatomical and physiological characteristics of the target tissue. Variability in tissue thickness, vascularity, elasticity, and mechanical load, compounded by patient-specific factors and intraoperative dynamics, demands constant vigilance and adaptability from the surgeon. The integration of AI and digital technologies offers potential improvements in refining this process. By providing real-time feedback on tissue properties and supporting intraoperative decision-making, these tools can assist surgeons in optimizing staple-line integrity and minimizing complications. The ongoing combination of surgical expertise with intelligent technology may contribute to advancing precision stapling in metabolic and bariatric surgery.

Background With over 1 billion individuals affected globally, obesity and obesity related diseases is now a leading cause of death. Metabolic and bariatric surgery (MBS) has emerged as a cornerstone intervention for severe obesity and its associated comorbidities. Despite its efficacy, postoperative care and follow-up after MBS remains highly variable worldwide. Objective The PARTNER study aimed to evaluate global clinical practices in the postoperative management following MBS by surveying multidisciplinary healthcare professionals. Methods This study was an international online survey conducted between October 2024 and January 2025. A multidisciplinary team developed the questionnaire based on existing literature and international guidelines. The survey assessed five domains: follow-up care, postoperative treatment, dietary management, patient support, and measurement of surgical outcomes. Responses were analysed descriptively. Results A total of 262 responses were received from 62 countries. Most respondents were bariatric surgeons (72.1%) working in public healthcare systems (73.3%). While 78.7% reported conducting three-month postoperative reviews, only 23.7% offered indefinite follow-up. Hybrid models of care (virtual and in-person) were common (56.9%). VTE prophylaxis and postoperative PPI use were recommended by 64.1% and 84.3% respectively. Nearly all respondents (98.1%) provided dietary advice, with protein and micronutrient supplementation widely endorsed. Only 56.1% routinely referred patients for psychological follow-up. Definitions of surgical success and failure varied widely, with inconsistent objective outcome measures. Conclusion The PARTNER study reveals significant international variation in postoperative management practices following MBS. These findings underscore the need for more standardized, evidence-based guidelines to improve long-term outcomes and equity of care worldwide.

Artificial intelligence (AI) is revolutionizing stapling in metabolic and bariatric surgery by transforming it from a manual skill into a data-driven, intelligent process. Its primary role is to enhance patient safety and improve surgical outcomes. Using real-time data from smart surgical staplers, AI algorithms analyze tissue properties like thickness and perfusion at the precise moment of stapling. This allows the system to predict potential complications, such as staple line leaks or bleeding, before they happen. Based on this analysis, AI can provide the surgeon with personalized, real-time feedback, recommending the optimal stapler cartridge, staple height, and compression time for that specific patient’s tissue. This intelligent guidance minimizes human error, reduces the risk of life-threatening complications, and paves the way for more consistent and successful long-term results.