Esophageal cancer (EC) is the seventh most common cancer and the sixth cause of cancer death worldwide. Histologically, esophageal squamous cell carcinoma (ESCC) and esophageal adenocarcinoma (EAC) account for up to 90% and 20% of all ECs, respectively. Clinical symptoms such as dysphagia, odynophagia, and bolus impaction occur late in the natural history of the disease, and the diagnosis is often delayed. The prognosis of ESCC and EAC is poor in advanced stages, being survival rates less than 20% at five years. However, when the diagnosis is achieved early, curative treatment is possible, and survival exceeds 80%. For these reasons, mass screening strategies for EC are highly desirable, and several options are currently under investigation. Blood biomarkers offer an inexpensive, non-invasive screening strategy for cancers, and novel technologies have allowed the identification of candidate markers for EC. The esophagus is easily accessible via endoscopy, and endoscopic imaging represents the gold standard for cancer surveillance. However, lesion recognition during endoscopic procedures is hampered by interobserver variability. To fill this gap, artificial intelligence (AI) has recently been explored and provided encouraging results. In this review, we provide a summary of currently available options to achieve early diagnosis of EC, focusing on blood biomarkers, advanced endoscopy, and AI.

Summary: Background: Artificial intelligence (AI) has recently been applied to endoscopy and questionnaires for the evaluation of oesophageal diseases (ODs).Aim: We performed a systematic review with meta‐analysis to evaluate the performance of AI in the diagnosis of malignant and benign OD.Methods: We searched MEDLINE, EMBASE, EMBASE Classic and the Cochrane Library. A bivariate random‐effect model was used to calculate pooled diagnostic efficacy of AI models and endoscopists. The reference tests were histology for neoplasms and the clinical and instrumental diagnosis for gastro‐oesophageal reflux disease (GERD). The pooled area under the summary receiver operating characteristic (AUROC), sensitivity, specificity, positive and negative likelihood ratio (PLR and NLR) and diagnostic odds ratio (DOR) were estimated.Results: For the diagnosis of Barrett's neoplasia, AI had AUROC of 0.90, sensitivity 0.89, specificity 0.86, PLR 6.50, NLR 0.13 and DOR 50.53. AI models’ performance was comparable with that of endoscopists (P = 0.35). For the diagnosis of oesophageal squamous cell carcinoma, the AUROC, sensitivity, specificity, PLR, NLR and DOR were 0.97, 0.95, 0.92, 12.65, 0.05 and DOR 258.36, respectively. In this task, AI performed better than endoscopists although without statistically significant differences. In the detection of abnormal intrapapillary capillary loops, the performance of AI was: AUROC 0.98, sensitivity 0.94, specificity 0.94, PLR 14.75, NLR 0.07 and DOR 225.83. For the diagnosis of GERD based on questionnaires, the AUROC, sensitivity, specificity, PLR, NLR and DOR were 0.99, 0.97, 0.97, 38.26, 0.03 and 1159.6, respectively.Conclusions: AI demonstrated high performance in the clinical and endoscopic diagnosis of OD.

Eosinophilic esophagitis (EoE) is a chronic type 2-mediated inflammatory disease of the esophagus that represents the most common eosinophilic gastrointestinal disease. Experts in the field of EoE across Italy (i.e., EoETALY Consensus Group) including gastroenterologists, endoscopists, allergologists/immunologists, and paediatricians conducted a Delphi process to develop updated consensus statements for the management of patients with EoE and update the previous position paper of the Italian Society of Gastroenterology (SIGE) in light of recent evidence. Grading of the strength and quality of the evidence of the recommendations was performed using accepted GRADE criteria. The guideline is divided in two documents: Part 1 includes three chapters, namely 1) definition, epidemiology, and pathogenesis; 2) clinical presentation and natural history, and 3) diagnosis, while Part 2 includes two chapters: 4) treatment and 5) monitoring and follow-up. This document has received the endorsement of three Italian national societies including the SIGE, the Italian Society of Neurogastroenterology and Motility (SINGEM), and the Italian Society of Allergology, Asthma, and Clinical Immunology (SIAAIC). With regards to patients' involvement, these guidelines involved the contribution of members of ESEO Italia, the Italian Association of Families Against EoE.

Background There is currently no recommendation regarding preferred drugs for active eosinophilic oesophagitis (EoE) because their relative efficacy is unclear. We conducted an up-to-date network meta-analysis to compare proton pump inhibitors, off-label and EoE-specific topical steroids, and biologics in EoE. Methods We searched MEDLINE, Embase, Embase Classic and the Cochrane Central Register of Controlled Trials from inception to June 2023. We included randomised controlled trials (RCTs) comparing efficacy of all drugs versus each other, or placebo, in adults and adolescents with active EoE. Results were reported as pooled relative risks with 95% CIs to summarise effect of each comparison tested, with drugs ranked according to P score Results Seventeen RCTs were eligible for systematic review. Of these, 15 studies containing 1813 subjects with EoE reported extractable data for the network meta-analysis. For histological remission defined as ≤6 eosinophils/high-power field (HPF), lirentelimab 1 mg/kg monthly ranked first. For histological remission defined as ≤15 eosinophils/HPF, budesonide orally disintegrating tablet (BOT) 1 mg two times per day ranked first. For failure to achieve symptom improvement, BOT 1 mg two times per day and budesonide oral suspension (BOS) 2 mg two times per day were significantly more efficacious than placebo. For failure to achieve endoscopic improvement based on the EoE Endoscopic Reference Score, BOT 1 mg two times per day and BOS 1 mg two times per day or 2 mg two times per day were significantly more efficacious than placebo. Conclusions Although this network meta-analysis supports the efficacy of most available drugs over placebo for EoE treatment, significant heterogeneity in eligibility criteria and outcome measures among available trials hampers the establishment of a solid therapeutic hierarchy.

The present document constitutes Part 2 of the EoETALY Consensus Statements guideline on the diagnosis and management of eosinophilic esophagitis (EoE) developed by experts in the field of EoE across Italy (i.e., EoETALY Consensus Group). Part 1 was published as a different document, and included three chapters discussing 1) definition, epidemiology, and pathogenesis; 2) clinical presentation and natural history and 3) diagnosis of EoE. The present work provides guidelines on the management of EoE in two final chapters: 4) treatment and 5) monitoring and follow-up, and also includes considerations on knowledge gaps and a proposed research agenda for the coming years. The guideline was developed through a Delphi process, with grading of the strength and quality of the evidence of the recommendations performed according to accepted GRADE criteria.This document has received the endorsement of three Italian national societies including the Italian Society of Gastroenterology (SIGE), the Italian Society of Neurogastroenterology and Motility (SINGEM), and the Italian Society of Allergology, Asthma, and Clinical Immunology (SIAAIC). The guidelines also involved the contribution of members of ESEO Italia, the Italian Association of Families Against EoE.

Artificial intelligence (AI) has enormous potential to support clinical routine workflows and therefore is gaining increasing popularity among medical professionals. In the field of gastroenterology, investigations on AI and computer-aided diagnosis (CAD) systems have mainly focused on the lower gastrointestinal (GI) tract. However, numerous CAD tools have been tested also in upper GI disorders showing encouraging results. The main application of AI in the upper GI tract is endoscopy; however, the need to analyze increasing loads of numerical and categorical data in short times has pushed researchers to investigate applications of AI systems in other upper GI settings, including gastroesophageal reflux disease, eosinophilic esophagitis, and motility disorders. AI and CAD systems will be increasingly incorporated into daily clinical practice in the coming years, thus at least basic notions will be soon required among physicians. For noninsiders, the working principles and potential of AI may be as fascinating as obscure. Accordingly, we reviewed systematic reviews, meta-analyses, randomized controlled trials, and original research articles regarding the performance of AI in the diagnosis of both malignant and benign esophageal and gastric diseases, also discussing essential characteristics of AI.