Liver regeneration after portal and hepatic vein embolization improves overall survival compared with portal vein embolization alone: mid-term survival analysis of the multicentre DRAGON 0 cohort.

BACKGROUND: The purpose of this study was to compare 3-year overall survival after simultaneous portal (PVE) and hepatic vein (HVE) embolization versus PVE alone in patients undergoing liver resection for primary and secondary cancers of the liver. METHODS: In this multicentre retrospective study, all DRAGON 0 centres provided 3-year follow-up data for all patients who had PVE/HVE or PVE, and were included in DRAGON 0 between 2016 and 2019. Kaplan-Meier analysis was undertaken to assess 3-year overall and recurrence/progression-free survival. Factors affecting survival were evaluated using univariable and multivariable Cox regression analyses. RESULTS: In total, 199 patients were included from 7 centres, of whom 39 underwent PVE/HVE and 160 PVE alone. Groups differed in median age (P = 0.008). As reported previously, PVE/HVE resulted in a significantly higher resection rate than PVE alone (92 versus 68%; P = 0.007). Three-year overall survival was significantly higher in the PVE/HVE group (median survival not reached after 36 months versus 20 months after PVE; P = 0.004). Univariable and multivariable analyses identified PVE/HVE as an independent predictor of survival (univariable HR 0.46, 95% c.i. 0.27 to 0.76; P = 0.003). CONCLUSION: Overall survival after PVE/HVE is substantially longer than that after PVE alone in patients with primary and secondary liver tumours.

Applying augmented reality in teaching of surgical residents-telementoring, a "stress-free" way to surgical autonomy?

BACKGROUND: Achieving surgical autonomy can be considered the ultimate goal of surgical training. Innovative head-mounted augmented reality (AR) devices enable visualization of the operating field and teaching from remote. Therefore, utilization of AR glasses may be a novel approach to achieve autonomy. The aim of this pilot study is to analyze the feasibility of AR application in surgical training and to assess its impact on intraoperative stress. METHODS: A head-mounted RealWear Navigator® 500 glasses and the TeamViewer software were used. Initial "dry lab" testing of AR glasses was performed in combination with the Symbionix LAP Mentor™. Subsequently, residents performed various stage-adapted surgical procedures semi-autonomously (SA) (on-demand consultation of senior surgeon, who is in theatre but not scrubbed) versus permanent remote supervision (senior surgeon not present) via augmented reality (AR) glasses, worn by the resident in theatre. Stress was measured by intraoperative heart rate (Polar® pulse belt) and State-Trait Anxiety Inventory (STAI) questionnaire. RESULTS: After "dry lab" testing, N = 5 senior residents performed equally N = 25 procedures SA and with AR glasses. For both, open and laparoscopic procedure AR remote assistance showed satisfactory applicability. Utilization of AR significantly reduced intraoperative peak pulse rate from 131 to 119 bpm (p = 0.004), as compared with the semi-autonomous group. Likewise, subjectively perceived stress according to STAI was significantly lower in the AR group (p = 0.011). CONCLUSION: AR can be applied in surgical training and may help to reduce stress in theatre. In the future, AR has a huge potential to become a stepping stone to surgical autonomy.

Recurrent aneurysmatic bleeding of pancreaticoduodenal aneurysm due to median arcuate ligament syndrome: a case report.

Median arcuate ligament syndrome (MALS) involves coeliac artery compression, causing a range of symptoms from chronic pain to life-threatening complications. This case features a 52-year-old patient with recurrent retroperitoneal bleeding from MALS-related inferior pancreaticoduodenal artery aneurysms (PDAAs). Emergency interventions, including surgical bleeding control, angioplasty, percutaneous drainage, and median arcuate ligament release, were conducted. The case highlights challenges in diagnosing and managing MALS-related PDAA, emphasizing the importance of early identification and tailored interventions based on clinical symptoms and imaging. Surgical intervention to release the ligament is the primary treatment, with considerations for prophylactic intervention in PDAA cases. Lack of established PDAA management protocols underscores the need for prompt intervention to prevent complications. In conclusion, this report stresses the association between MALS and PDAA, advocating for early identification and tailored management to mitigate complications.

Cecal volvolus herniation through the Winslow Foramen: a case report and literature review of surgical management.

Both cecal volvolus and Winslow hernia are rare clinical presentations accounting for 1-1.5 and 0.08% of bowel obstructions. The combination of the two phenomena has been described so far in 13 case reports. Our patient underwent laparotomy with lesser Sac opening, manual hernia reduction, right hemicolectomy and partial Foramen closure with two simple stitches of PDS 4.0. Due to the scarcity of literature guidelines are not available, the intraoperative state of the tissues and the likelihood of a hernia recurrence play a decisive role in surgical management.

A Predictive Noninvasive Single-Nucleotide Variation-Based Biomarker Signature for Resectable Pancreatic Cancer: Protocol for a Prospective Validation Study.

BACKGROUND: Single-nucleotide variations (SNVs; formerly SNPs) are inherited genetic variants that can be easily determined in routine clinical practice using a simple blood or saliva test. SNVs have potential to serve as noninvasive biomarkers for predicting cancer-specific patient outcomes after resection of pancreatic ductal adenocarcinoma (PDAC). Two recent analyses led to the identification and validation of three SNVs in the CD44 and CHI3L2 genes (rs187115, rs353630, and rs684559), which can be used as predictive biomarkers to help select patients most likely to benefit from pancreatic resection. These variants were associated with an over 2-fold increased risk for tumor-related death in three independent PDAC study cohorts from Europe and the United States, including The Cancer Genome Atlas cohorts (reaching a P value of 1×10-8). However, these analyses were limited by the inherent biases of a retrospective study design, such as selection and publication biases, thereby limiting the clinical use of these promising biomarkers in guiding PDAC therapy. OBJECTIVE: To overcome the limitations of previous retrospectively designed studies and translate the findings into clinical practice, we aim to validate the association of the identified SNVs with survival in a controlled setting using a prospective cohort of patients with PDAC following pancreatic resection. METHODS: All patients with PDAC who will undergo pancreatic resection at three participating hospitals in Switzerland and fulfill the inclusion criteria will be included in the study consecutively. The SNV genotypes will be determined using standard genotyping techniques from patient blood samples. For each genotyped locus, log-rank and Cox multivariate regression tests will be performed, accounting for the relevant covariates American Joint Committee on Cancer stage and resection status. Clinical follow-up data will be collected for at least 3 years. Sample size calculation resulted in a required sample of 150 patients to sufficiently power the analysis. RESULTS: The follow-up data collection started in August 2019 and the estimated end of data collection will be in May 2027. The study is still recruiting participants and 142 patients have been recruited as of November 2023. The DNA extraction and genotyping of the SNVs will be performed after inclusion of the last patient. Since no SNV genotypes have been determined, no data analysis has been performed to date. The results are expected to be published in 2027. CONCLUSIONS: This is the first prospective study of the CD44 and CHI3L2 SNV-based biomarker signature in PDAC. A prospective validation of this signature would enable its clinical use as a noninvasive predictive biomarker of survival after pancreatic resection that is readily available at the time of diagnosis and can assist in guiding PDAC therapy. The results of this study may help to individualize treatment decisions and potentially improve patient outcomes. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/54042.

A machine learning predictive model for recurrence of resected distal cholangiocarcinoma: Development and validation of predictive model using artificial intelligence.

INTRODUCTION: Distal Cholangiocarcinoma (dCCA) represents a challenge in hepatobiliary oncology, that requires nuanced post-resection prognostic modeling. Conventional staging criteria may oversimplify dCCA complexities, prompting the exploration of novel prognostic factors and methodologies, including machine learning algorithms. This study aims to develop a machine learning predictive model for recurrence after resected dCCA. MATERIAL AND METHODS: This retrospective multicentric observational study included patients with dCCA from 13 international centers who underwent curative pancreaticoduodenectomy (PD). A LASSO-regularized Cox regression model was used to feature selection, examine the path of the coefficient and create a model to predict recurrence. Internal and external validation and model performance were assessed using the C-index score. Additionally, a web application was developed to enhance the clinical use of the algorithm. RESULTS: Among 654 patients, LNR (Lymph Node Ratio) 15, neural invasion, N stage, surgical radicality, and differentiation grade emerged as significant predictors of disease-free survival (DFS). The model showed the best discrimination capacity with a C-index value of 0.8 (CI 95 %, 0.77%-0.86 %) and highlighted LNR15 as the most influential factor. Internal and external validations showed the model's robustness and discriminative ability with an Area Under the Curve of 92.4 % (95 % CI, 88.2%-94.4 %) and 91.5 % (95 % CI, 88.4%-93.5 %), respectively. The predictive model is available at https://imim.shinyapps.io/LassoCholangioca/. CONCLUSIONS: This study pioneers the integration of machine learning into prognostic modeling for dCCA, yielding a robust predictive model for DFS following PD. The tool can provide information to both patients and healthcare providers, enhancing tailored treatments and follow-up.