Introduction

The use of Immune Checkpoint Inhibitors (ICI’s) have been demonstrated to significantly improve progression free-survival in a number of malignancies ⁽¹⁾. Immune related adverse effects (IRAEs) are, however, an increasingly prevalent unwanted outcome, and now a widely recognised entity, with a number of guideline based management strategies published. Gastrointestinal IRAEs (GIIRAEs), including colitis and hepatitis are among two of the most reported IRAE’s with combination ICI use accounting for 15% and 10% of all IRAE’s respectively ⁽²⁾.

Aims & Methods

We aimed to report the rates and severity of Gastrointestinal Immune Related Adverse Effects (GIIRAE’s) for patients who had received combination immunotherapy with nivolumab and ipilimumab, and analyse the investigations, treatment regime and response in a single tertiary referral centre. A retrospective cohort analysis was conducted of all patients receiving ipilimumab and nivolumab for melanoma, renal cell carcinoma and mesothelioma between the dates of 2019 to 2023. The local oncology database and electronic hospital records were reviewed. Baseline demographics, incidence, grade and treatment of GIIRAE’s were collected.

Results

A total of 99 patients on combination ICI were identified. 26 patients (26%) had a GIIRAE of whom 15% (15/99) had diarrhoea and 11% (11/99) had hepatitis. 47% (7/15) of patients with diarrhoea had radiological or endoscopic confirmation of colitis (5) or enteritis (2). Of the 5 patients with Grade 3-4 diarrhoea, 3 (60%) required escalation to infliximab, all of which only had mild colitis evident at colonoscopy. 7/11 (64%) of patients had Grade 3-4 hepatitis, with only one patient requiring escalation to steroid sparing agent, despite only 57% (4/7) being commenced on high dose intravenous steroids as recommended by current guidelines. 40% (10/26) of Grade 1-2 IRAEs were managed conservatively. Only 50% (13/26) of all identified cases had involvement of a gastroenterologist.

Conclusion

Management strategies for GIIRAEs have and continue to evolve, and our cohort highlights the heterogeneity in investigation and management practice over the course of four years. Based on our findings, symptom-based assessment of severity for diarrhoea associated IRAEs appears to be a better predictor for the need for escalation of therapy rather than severity of investigation findings given only mild colitis was found on endoscopy for all patients who required biologic therapy. For hepatitis associated IRAEs, it would appear that a less aggressive approach to steroid therapy compared to current published guidelines may be adequate. Further prospective studies with larger cohort would be of value to confirm our observations.

References

1) Sedano R, Cabrera D, Jiménez A, Ma C, Jairath V, Arrese M, Arab JP. Immunotherapy for Cancer: Common Gastrointestinal, Liver, and Pancreatic Side Effects and Their Management. Am J Gastroenterol. 2022 Dec 1;117(12):1917-1932. doi: 10.14309/ajg.0000000000001983. Epub 2022 Aug 22. PMID: 36455219.
2) Dahiya DS, Wani F, Guidi JC, Kichloo A. Gastrointestinal Adverse Effects of Immunotherapeutic Agents: A Systematic Review. Gastroenterology Res. 2020 Dec;13(6):227-232. doi: 10.14740/gr1340. Epub 2020 Dec 23. PMID: 33447301; PMCID: PMC7781271.

Introduction

​Helicobacter pylori is class I carcinogen responsible for 89% of non cardia gastric cancer.

Aims & Methods

Screen and treat application study is ongoing in six EU countries in the asymptomatic age group of citizens from the age of 30 to 34 years. The test-and-treat program is free of charge for all eligible participants. Four different locally validated H. pylori IgG serologies and / or UBT as a test were used. Quadruple bismuth based 14 day, a 10-day regimen with esomeprazole combined with bismuth quadruple therapy in a single-capsule or 14 day tripple eradication therapy were used as the first line therapy. RED Cap IT platform (pretreatment questionnaires, treatment protocols, side effects, compliance,complications…) was used to collect data for quality assurance, monitoring, and evaluation.

Results

The study is still ongoing. Interim results show a wide variation of response rates to the invitation (from 4,1% in Wroclaw to 45,5% in Ljubljana). Seropositivity / UBT rates vary from 13,9% in Ljubljana to 49% in Latvia. The eradication rates are from 88,2% in Dublin to 96,9% in Ljubljana. The use of all three treatment schemes showed encouraging eradication and adherence rates, whereas the incidence of adverse events due to this therapy was within the acceptable range. No serious adverse events have been reported so far. The differences among study sites are in the organisation of invitations, educational campaigns and dissemination activities as well as in the time when study was launched in a certain country.

Conclusion

The screen and treat application study is organised in a way as an organised cancer screening program. Careful study planning: legal, financial, technical, organisational aspect and public awareness campains should be implemented before official lounch of the study.Central data capture system is necessary to implement quality control monitoring. Education and public awareness campaign to increase the participation rate are of crucial importance.

Introduction

Upper gastrointestinal bleeding (UGIB) remains a common and potentially life-threatening condition in emergency medicine. Early identification of patients requiring urgent esophagogastroduodenoscopy (EGD) is crucial to improve outcomes and optimize healthcare resources management. Traditional risk stratification tools, such as the Glasgow-Blatchford Score (GBS), have been widely used to guide clinical decision-making. However, the growing availability of artificial intelligence (AI) offers new perspectives in diagnostic and therapeutic strategies. Recent advancements in large language models (LLMs), such as ChatGPT® and Google GEMINI®, suggest potential applications in clinical triage.

Aims & Methods

This study aims to evaluate the performance of these AI tools in determining the appropriate timing for endoscopy in UGIB patients, compared to standard clinical scoring systems.
The medical records of 100 patients who presented to the emergency department with upper gastrointestinal bleeding (UGIB) between April and September 2024 were reviewed retrospectively. Each case was evaluated by two AI models: Google GEMINI® (September 2024 version) and ChatGPT® (version 3.5). These models, acting as virtual gastroenterology experts, were asked to recommend whether urgent or delayed esophagogastroduodenoscopy (EGD) was necessary. For each patient, clinical data such as age, medical history, clinical examination findings, and laboratory results at admission were provided. The AI recommendations were recorded in an Excel file and compared with the Glasgow-Blatchford Score (GBS), which was calculated by a gastroenterologist at the time of admission.
An urgent endoscopy was deemed necessary for a GBS ≥ 3. Statistical analysis was performed using Jamovi® software (version 2.3), and the agreement between the AI models and human evaluations was assessed using Cohen’s kappa coefficient (κ).

Results

The epidemiological characteristics of the patient cohort revealed a median age of 63 years (range: 37.5 to 78), with a male-to-female ratio of 1.7. Among the patients, 73% had at least one comorbidity, including hypertension (33%), diabetes (22%), ischemic heart disease (17%), and chronic liver disease (12%). At the time of admission, melena was the predominant symptom, reported in 78% of patients. The average Glasgow-Blatchford Score (GBS) was 7.07 ± 3.47.
In terms of AI recommendations, ChatGPT suggested urgent endoscopy in 48% of cases, while Google GEMINI recommended it in 49%. A moderate level of agreement was observed between the two AI models for urgent endoscopy recommendations (kappa = 0.540, p < 0.001). However, the agreement between the AI models and the human evaluator was weak. The level of agreement between the gastroenterologist’s assessment and ChatGPT’s recommendation was weak (kappa = 0.222, p < 0.001), with similar results for Google GEMINI (kappa = 0.231, p < 0.001).

Conclusion

Artificial intelligence is a promising tool that is already beginning to transform various fields of medicine. However, our study demonstrates that the publicly available AI models currently in use are not yet capable of effectively triaging patients in emergency settings, particularly in cases of upper gastrointestinal bleeding. A model specifically trained on clinical gastroenterology cases, using validated scientific guidelines, could produce more accurate results and potentially revolutionize patient triage in emergency departments.

Introduction

Endoscopy is essential for the early detection and management of upper gastrointestinal (UGI) tumors, yet variability in diagnostic expertise and workload-related fatigue often compromise the quality of report documentation. These challenges underscore the urgent need for innovative technological approaches to assist endoscopists in optimizing the report generation process.
Multimodal large language models (MLLMs) enable the synergistic integration of visual and semantic representations, demonstrating significant clinical value in medical text generation. This study introduces MERGS, an innovative endoscopy report generation system powered by MLLM. MERGS annotates medical terminology and generates natural language descriptions based on endoscopic images, ultimately producing structured reports that conform to clinical standards.

Aims & Methods

MERGS is built on the pre-trained MLLM Qwen-VL-2.5-7B. It adopts a multimodal architecture integrating visual encoding and cross-modal alignment to enable medical term annotation and natural language generation from endoscopic images. A pathological classification module further enhances lesion characterization. The language model synthesizes image-derived descriptions to generate a comprehensive report with a diagnostic conclusion.
UGI endoscopy reports were collected from the Sun Yat-sen University Cancer Center between 2019 and 2021. A standardized terminology dictionary was developed based on clinical guidelines, supporting a three-tier annotation framework: endoscopic image → standardized medical terminology → natural language text. Model-assisted pre-annotation combined with expert validation substantially reduced manual effort. The resulting dataset was split into training and test sets at an 8:2 ratio.
Model performance was assessed via multi-label classification metrics (accuracy, specificity, F1-score), as well as semantic similarity measures (Jaccard index) to evaluate its ability to recognize anatomical sites and endoscopic features. Natural language generation quality was assessed using BLEU-2 and ROUGE-L scores, and expert satisfaction ratings were collected for 100 randomly sampled outputs to assess readability and clinical relevance. A human–AI comparison was conducted on 50 test reports, comparing system-generated outputs with those authored by experienced endoscopists.

Results

A multimodal dataset was constructed after quality control, comprising 22,373 reports and 135,503 associated images from 21,619 patients. Internal validation demonstrated that MERGS achieved robust performance in automated image interpretation, with 97.3% accuracy and 98.6% specificity for standard anatomical site recognition. In feature identification, it showed high clinical relevance (F1 scores: smooth mucosa 0.915, polyp 0.857, tumor 0.878, erythematous mucosa 0.766).
Generated reports showed strong alignment with reference reports (BLEU-2 = 0.587, ROUGE-L = 0.661) and received high expert satisfaction ratings (mean score: 4.34 ± 0.63 on a 5-point scale). In a human–AI comparison, MERGS-generated reports were considered non-inferior to those authored by endoscopists (scores: MERGS 4.52 ± 0.81 vs. manual 4.68 ± 0.72), with the upper limit of the one-sided 95% CI (0.37) below the non-inferiority margin (0.5, p = 0.0045).

Conclusion

This study developed an automated system for UGI endoscopy reporting powered by an MLLM. Both internal and clinical validation demonstrated the system's high performance in endoscopic image feature recognition and report generation, underscoring its strong potential for clinical deployment.

References

Luo H, Xu G, Li C, He L, Luo L, Wang Z, Jing B, Deng Y, Jin Y, Li Y, Li B, Tan W, He C, Seeruttun S R, Wu Q, Huang J, Huang D W, Chen B, Lin S B, Chen Q M, Yuan C M, Chen H X, Pu H Y, Zhou F, He Y, Xu R H. Real-time artificial intelligence for detection of upper gastrointestinal cancer by endoscopy: a multicentre, case-control, diagnostic study[J]. Lancet Oncol, 2019,20(12):1645-1654.

Introduction

Clostridioides difficile infection (CDI) is among of the most common causes of nosocomial diarrhea with high morbidity and mortality. Recurrent and refractory disease courses in particular represent a special challenge in the management of these patients. The identification of patients at risk for recurrent courses could improve management, e.g. through the use of new and effective therapies in patients with CDI.

Aims & Methods

The aim of this study was to investigate the relationship between the presence of different comorbidities and the course of CDI.
In a retrospective, monocentric cohort study of a tertiary centre over a period of 10 years, patients with CDI-related hospitalizations were examined. The study included 336 patients with available information on comorbidities (diverticulosis, biliary diseases (cholecysto-, docholithiasis, cholecystitis, cholangitis or cholangiectasis) inflammatory bowel diseases (IBD), liver cirrhosis and malignancies) and the diagnosis of the recurrent CDI (rCDI) vs. non-recurrent course of CDI (nrCDI). Patients on dialysis or immunosuppressed patients were also considered as comorbid.

Results

Among 336 patients included in the study, 227 (67.6%) presented with at least one comorbidity. The most common were malignancy (30.4%), biliary diseases (19.4%), diverticulosis (15.8%), liver cirrhosis (14.3%), and inflammatory bowel disease (IBD, 8.9%). rCDI occurred more frequently in patients with diverticulosis and biliary disease. Diverticulosis was present in 24.4% of rCDI cases vs. 12.6% in nrCDI cases (OR 2.24, 95% CI 1.22–4.17). Biliary diseases were observed in 26.7% of rCDI vs. 16.7% of nrCDI patients (OR 1.82, 95% CI 1.01–3.26). No significant associations with rCDI were found for IBD (OR 0.66, 95% CI 0.27–1.65), malignancy (OR 1.13, 95% CI 0.67–1.88), or liver cirrhosis (OR 0.79, 95% CI 0.38–1.61).

Conclusion

These findings suggest diverticulosis and biliary disease may be independent risk factors for rCDI, whereas other common comorbidities are not significantly associated with recurrence. Structured assessment of risk conditions may help to select patients for tailored management to prevent rCDI disease course.