Research Institute against Digestive Cancer

This framework from the Society of Robotic Surgery offers best practices for safe, effective, and ethical telesurgery. It is based on a comprehensive literature review, Delphi consensus process, and an international meeting in Orlando, Florida, in February 2024, involving experts from a variety of domains including surgery, engineering, telecommunications, hospital administration, law, and regulatory affairs. Key areas covered include the global landscape and new models of remote surgical care, technological needs like connectivity and cybersecurity, safety standards, training and credentialing for surgical teams, and scalable implementation models. It also addresses legal and ethical issues, clinical indications, and regional and international regulatory comparisons. This framework aims to be a key reference for institutions, surgical societies, policymakers, and technology developers to promote safe, equitable, and sustainable telesurgery worldwide.

Robot-assisted procedures are increasingly common, and several systems are available for thoraco-abdominal surgery. Specific structured training is necessary, while access to these systems is still limited. This study aimed to assess surgeons' skill progress during consecutive training days of a curriculum with exposure to different robotic systems. This prospective observational study enrolled 47 surgeons with anonymized analysis of SimNow™ simulator performance scores and dedicated questionnaires after written consent. The primary outcome was the overall score, based on economy of motion, time to complete the exercise, and penalty for errors. Course participants in 2022-2023 had chosen 2 full hands-on days on Da Vinci® consoles with either virtual reality (VR) simulation training using the SimNow (n = 21, 44.7%) or digestive surgery procedures with a live animal model (n = 26, 55.3%). In all participants, training on Da Vinci® systems included console functions and principles of docking, camera, and instrument use for console and procedural training. They additionally had access to introductory dry-lab and VR simulator exercises on the Versius, HugoTMRAS, and Dexter systems and to VR exercises on the ROBOTiS simulator. The participants (16F/31M, median age 40years, range 29-58) from various surgical specialties (general/visceral/vascular) had no (n = 35, 74.5%) or little (n = 12, 25.5%) robotic experience including bedside assistance only and 20 (42.6%) had robotic simulator experience. The demographic variables fully completed by 44/47 participants (93.6%) and choice of module had no significant impact on the primary outcome. The considerable performance improvement from days 1 to 2 was exemplified by a significantly increased economy of motion and decreased amount of excessive force. Robotic surgical training is increasingly complex with several systems on the market. Within a dedicated robotic surgery curriculum and based on integrated performance metrics, a significant improvement of skill levels was observed in a relatively short period of time.

Introduction Vaginal approaches have become routine in the field of gynecologic surgery, whereas in general surgery vaginal wall transection is an infrequent practice typically reserved for extensive tumor resections. Approximately two decades ago, natural orifice transluminal endoscopic surgery (NOTES) revolutionized conventional boundaries by accessing the peritoneal cavity transorally, transrectally, or transvaginally, enabling general surgery without visible scars. Although transvaginal approaches have been successfully used for various abdominal procedures by general surgeons, a gap remains in comprehensive training to fully exploit the potential of this route. Material and methods PubMed, Google Scholar, and Scopus databases were searched to retrieve relevant articles illustrating how general surgeons can adeptly manage vaginal approaches. Results The article presents a practical framework for general surgeons to execute a complete vaginal approach, addressing the management of vaginal specimen extraction and vaginal cuff closure, even in the absence of an experienced gynecologist. Conclusion The evolution of abdominal surgery is moving towards less invasive techniques, emphasizing the importance of understanding the nuances and challenges associated with the vaginal route. This approach is linked to minimal oncological, sexual, and infective complications, and to the absence of pregnancy-related complications. Such knowledge becomes increasingly crucial, particularly with the renewed demand for transvaginal access in robot-assisted NOTES procedures.

Assessing the critical view of safety in laparoscopic cholecystectomy requires accurate identification and localization of key anatomical structures, reasoning about their geometric relationships to one another, and determining the quality of their exposure. Prior works have approached this task by including semantic segmentation as an intermediate step, using predicted segmentation masks to then predict the CVS. While these methods are effective, they rely on extremely expensive ground-truth segmentation annotations and tend to fail when the predicted segmentation is incorrect, limiting generalization. In this work, we propose a method for CVS prediction wherein we first represent a surgical image using a disentangled latent scene graph, then process this representation using a graph neural network. Our graph representations explicitly encode semantic information - object location, class information, geometric relations - to improve anatomy-driven reasoning, as well as visual features to retain differentiability and thereby provide robustness to semantic errors. Finally, to address annotation cost, we propose to train our method using only bounding box annotations, incorporating an auxiliary image reconstruction objective to learn fine-grained object boundaries. We show that our method not only outperforms several baseline methods when trained with bounding box annotations, but also scales effectively when trained with segmentation masks, maintaining state-of-the-art performance.

Introduction/BackgroundObesity represents an exponentially growing preventable disease leading to different health complications, particularly when associated with cancer disease. In recent years, however, an ‘obesity paradox’ has been hypothesized where obese individuals affected by cancer counterintuitively show better survival rates. The aim of this systematic review and meta-analysis is to assess whether the prognosis in gynecological malignancies is positively influenced by obesity.MethodologyThis study adheres to PRISMA guidelines and is registered with PROSPERO. Studies reporting the impact of a BMI > 30 kg/m2 compared with < 30 kg/m2 in patients with gynecological cancers querying PubMed, Google Scholar and ClinicalTrials.gov were included in the analysis. The Quality Assessment of Diagnostic Accuracy Studies 2 tool (QUADAS-2) was used for quality assessment for the selected articles.ResultsTwenty-one studies were identified, including 14108 patients with cervical (CC), ovarian (OC) and endometrial cancer (EC) for the meta-analysis. There was no benefit in 5-year overall survival (OS) for obese patients compared with non-obese (OR = 1.2, 95% CI 1.00–1.44, p =0.05; I2 = 71%). When pooling for cancer subgroups, there were no statistically significative differences in 5-year OS in CC and 5-year OS and progression-free survival (PFS) in OC patients. For obese women affected by EC, a significant decrease of 44% in 5-year OS (p=0.01) was revealed, without significant differences for 5-year disease-free survival (DFS) (p =0.78).ConclusionAccording to the results of the present mete-analysis a BMI ≥ 30 kg/m2 does not have a positive prognostic effect on survival compared with a BMI <30 kg/m2 in women affected by gynecological cancers. The existence of the ‘obesity paradox’ in other fields, however, suggests the importance of further investigations with prospective studies.DisclosuresNone.

BackgroundThe large amount of heterogeneous data collected in surgical/endoscopic practice calls for data-driven approaches as machine learning (ML) models. The aim of this study was to develop ML models to predict endoscopic sleeve gastroplasty (ESG) efficacy at 12 months defined by total weight loss (TWL) % and excess weight loss (EWL) % achievement. Multicentre data were used to enhance generalizability: evaluate consistency among different center of ESG practice and assess reproducibility of the models and possible clinical application. Models were designed to be dynamic and integrate follow-up clinical data into more accurate predictions, possibly assisting management and decision-making.MethodsML models were developed using data of 404 ESG procedures performed at 12 centers across Europe. Collected data included clinical and demographic variables at the time of ESG and at follow-up. Multicentre/external and single center/internal and temporal validation were performed. Training and evaluation of the models were performed on Python’s scikit-learn library. Performance of models was quantified as receiver operator curve (ROC-AUC), sensitivity, specificity, and calibration plots.ResultsMulticenter external validation: ML models using preoperative data show poor performance. Best performances were reached by linear regression (LR) and support vector machine models for TWL% and EWL%, respectively, (ROC-AUC: TWL% 0.87, EWL% 0.86) with the addition of 6-month follow-up data.Single-center internal validation: Preoperative data only ML models show suboptimal performance. Early, i.e., 3-month follow-up data addition lead to ROC-AUC of 0.79 (random forest classifiers model) and 0.81 (LR models) for TWL% and EWL% achievement prediction, respectively. Single-center temporal validation shows similar results.ConclusionsAlthough preoperative data only may not be sufficient for accurate postoperative predictions, the ability of ML models to adapt and evolve with the patients changes could assist in providing an effective and personalized postoperative care. ML models predictive capacity improvement with follow-up data is encouraging and may become a valuable support in patient management and decision-making.

The blunt and penetrating abdominal trauma follow-up relies on contrast CT scan to detect solid organ injuries. CEUS is not widely used worldwide, despite its accuracy and feasibility in identifying parenchymal and vascular lesions. This survey aims to define the current use of CEUS in Trauma Centers and the expectations of Emergency Surgeons regarding its role. We sent an online questionnaire composed of 10 questions to trauma physicians worldwide during the period of September 2022 to March 2023. 59 participants from 53 different centers answered the questionnaire. 76.2% of the enrolled participants have CEUS available at their Institution, and 49.2% think that it can replace CT scan. Contrast-CT scan remains the preferred option in the follow-up of blunt liver (69.5%), spleen (72.9%) and kidney (76.3%) injuries, followed by CEUS which is used in 18.6% for liver, 20.3% for spleen and 15.3% for kidney injuries. The results are similar for penetrating abdominal trauma, with contrast CT scan being the first-line imaging technique (91.5% for liver, 91.5% for spleen and 88.1% for kidney injuries), followed by CEUS (5.1% for liver, 5.1% for spleen and 6.8% for kidney injuries). Our survey shows that CEUS is still underutilized in the follow-up of abdominal trauma, even though many emergency surgeons consider it as a valid alternative to contrast CT scan.

An ischemia-reperfusion injury (IRI) results from a prolonged ischemic insult followed by the restoration of blood perfusion, being a common cause of morbidity and mortality, especially in liver transplantation. At the maximum of the potential damage, IRI is characterized by 2 main phases. The first is the ischemic phase, where the hypoxia and vascular stasis induces cell damage and the accumulation of damage-associated molecular patterns and cytokines. The second is the reperfusion phase, where the local sterile inflammatory response driven by innate immunity leads to a massive cell death and impaired liver functionality. The ischemic time becomes crucial in patients with underlying pathophysiological conditions. It is possible to compare this process to a shooting gun, where the loading trigger is the ischemia period and the firing shot is the reperfusion phase. In this optic, this article aims at reviewing the main ischemic events following the phases of the surgical timeline, considering the consequent reperfusion damage.