Background and Aims: International guidelines advise improving esophagogastroduodenoscopy (EGD) quality in Western countries, where gastric cancer is still diagnosed in advanced stages. This nationwide study investigated some indicators for the quality of EGD performed in endoscopic centers in Italy. Methods: Clinical, endoscopic, and procedural data of consecutive EGDs performed in one month in the participating centers were reviewed and collected in a specific database. Some quality indicators before and during endoscopic procedures were evaluated. Results: A total of 3,219 EGDs performed by 172 endoscopists in 28 centers were reviewed. Data found that some relevant information (family history for GI cancer, smoking habit, use of proton pump inhibitors) were not collected before endoscopy in 58.5-80.7% of patients. Pre-endoscopic preparation for gastric cleaning was routinely performed in only 2 (7.1%) centers. Regarding the procedure, sedation was not performed in 17.6% of patients, and virtual chromoendoscopy was frequently (>75%) used in only one (3.6%) center. An adequate sampling of the gastric mucosa (i.e., antral and gastric body specimens) was heterogeneously performed, and it was routinely performed only by 23% of endoscopists, and in 14.3% centers. Conclusions: Our analysis showed that the quality of EGD performed in clinical practice in Italy deserves to be urgently improved in different aspects.

Background and Objectives: Malignant double obstruction, defined as the coexistence of malignant biliary obstructio (MBO) and malignant gastric outlet obstruction (mGOO), frequently complicates advanced pancreatic and periampullary malignancies. Because biliary and duodenal obstructions are anatomically and functionally interconnected, treatment of one may influence the outcome of the other. This review aims to summarize current evidence on endoscopic management strategies and to propose an anatomy-driven approach integrating conventional and endoscopic ultrasound (EUS)-guided techniques. Materials and Methods: A narrative review of the current literature was performed, focusing on endoscopic approaches for malignant biliary and gastric outlet obstruction. Available evidence regarding endoscopic retrograde cholangiopancreatography (ERCP), EUS-guided biliary drainage (EUS-BD), enteral self-expandable metal stenting, and EUS-guided gastroenterostomy (EUS-GE) was critically analyzed, focusing on technical feasibility, clinical outcomes, adverse events, and therapeutic sequencing. Results: ERCP remains an effective option when papillary access is preserved; however, duodenal obstruction, especially when involving the papilla, represents a major limitation. EUS-BD has emerged as a reliable alternative after failed ERCP and may provide a primary drainage strategy in selected patients. In the setting of concomitant gastric outlet obstruction, EUS-guided hepaticogastrostomy may offer advantages over choledochoduodenostomy by avoiding the obstructed duodenal pathway. For malignant gastric outlet obstruction, enteral stenting provides rapid symptom relief, but is associated with limited long-term durability. EUS-GE has demonstrated high technical and clinical success rates, lower rates of recurrent obstruction compared with enteral stenting, and outcomes comparable to surgical gastrojejunostomy with reduced invasiveness. These findings support an integrated EUS-based approach for selected patients. Conclusions: Malignant double obstruction should be considered a single anatomofunctional entity rather than two independent conditions. An individualized, anatomy-driven strategy combining EUS-guided biliary drainage and EUS-GE may represent the future direction of endoscopic palliation, allowing durable internal bypass and facilitating oncological management. Further prospective studies are required to define optimal treatment sequencing and patient selection.

Background: Machine learning (ML) is increasingly applied in medicine, underscoring the need for transparent and clinically relevant models. In gastrointestinal oncology, most ML studies rely on raw imaging data, which limits clinical adoption due to poor interpretability and the difficulty of collecting high-quality, large-scale video and image datasets in routine practice. Endoscopic ultrasound (EUS) plays a central role in the evaluation of pancreatic cancer; however, structured EUS features remain underused in predictive modeling. Objective: To assess the performance and interpretability of ML models for diagnosing pancreatic ductal adenocarcinoma (PDAC) using routinely collected EUS variables. Methods: We conducted a retrospective multicenter study using data from two Italian hospitals (n = 641) for model training and internal validation and from a third hospital (n = 120) for external validation, collected from 2015 to 2023. Decision trees, random forests, naïve Bayes and other classifiers were developed and evaluated. Model performance was assessed in terms of discriminative ability, calibration, and selective prediction. Results: All models demonstrated high discriminative performance (AUC ≥ 0.90). Decision trees provided the most favorable balance between interpretability and accuracy (balanced accuracy = 0.87; sensitivity = 0.89). Calibration and selective prediction analyses confirmed the robustness of the models. Conclusions: These findings demonstrate the feasibility of implementing interpretable yet high-performing ML models for PDAC diagnosis in real-life endoscopic settings.