Introduction

Gastroesophageal Reflux Disease (GERD) is a common condition diagnosed by endoscopy and/or pH monitoring. However, only high-resolution manometry (HRM) provides detailed assessment of the pathophysiology underlying reflux mechanisms and GERD symptoms. While it is widely accepted that esophago-gastric junction (EGJ) type 3 is a strong predictor of GERD, EGJ type 2 is less clearly implicated in GERD pathogenesis¹. This study aimed to investigate the clinical and physiological characteristics of patients with EGJ type 2 and to identify HRM variables that predict GERD in this category of patients.

Aims & Methods

535 patients with suspected GERD who underwent HRM and reflux monitoring were retrospectively analyzed. Patients were divided into three groups based on EGJ morphology (type 1, 2, or 3). Clinical data, HRM parameters, and reflux study results were compared among groups. The Milan Score², a novel metric that integrates ineffective esophageal motility (IEM), EGJ-contractile integral (EGJ-CI), EGJ morphology and straight leg raise (SLR) response, was applied to the EGJ type 2 group to identify patients at higher risk of developing GERD. Pathologic GERD was defined as AET >6% on pH-impedance monitoring and/or stricture, Barrett’s esophagus or Los Angeles grade B, C or D, or at endoscopy. As previously demonstrated, a Milan Score of 137 (risk rate 50%) or higher was considered pathologic. Receiver operating characteristic (ROC) analysis was performed to determine Milan score prediction of GERD within patients with EGJ type 2 morphology.

Results

Patients with EGJ type 3 had the highest rate of GERD diagnosis, followed by EGJ type 2 and type 1 (p <0.001). HRM showed differences in EGJ-CI, proportion of patients with IEM and positive SLR response among groups (p <0.001). Reflux monitoring revealed a stepwise increase in AET from type 1 to 3 (5.1±7.3 vs. 8.3±11.1 vs. 12.2±11.3, p <0.001). A pathologic Milan Score identified two subgroups of patients within the EGJ type 2 group: patients with a score below 137 resembled type 1, while those with a score of 137 or higher resembled type 3 in terms of pH monitoring characteristics (Table). On ROC analysis the area under the curve was 0.858, and the optimal threshold to identify pathologic GERD in this group was 137 points (sensitivity 77.3%, specificity 87.7%).

Conclusion

Our data suggests that EGJ type 2 encompasses a wide spectrum of disease severity. The Milan Score with a cut-off of 137 may help to identify patients with EGJ type 2 who are more likely to demonstrate pathologic GERD on reflux monitoring.

References

1. Tolone S, de Cassan C, de Bortoli N, et al. Esophagogastric junction morphology is associated with a positive impedance-pH monitoring in patients with GERD. Neurogastroenterol Motil. 2015 Aug;27(8):1175-82.
2. Siboni S, Sozzi M, Kristo I et al. The Milan score: A novel manometric tool for a more efficient diagnosis of gastro-esophageal reflux disease. United European Gastroenterol J. 2024 Mar 27. doi: 10.1002/ueg2.12565. Epub ahead of print. PMID: 38536701.

Introduction

Computer-Aided Diagnosis (CADx) systems have become available to assist endoscopists with optical polyp diagnosis. However, current CADx systems have not been trained to recognize sessile serrated lesions (SSLs) and currently classify SSLs incorrectly either as hyperplastic or adenomatous. We aimed to assess the effect of current CADx systems on diagnostic accuracy of SSL recognition among a diverse group of endoscopists.

Aims & Methods

We conducted a prospective video-based study including 17 endoscopists (gastroenterologists and surgeons, experts, and trainees). Each endoscopist reviewed the same video and image series of 115 polyps (33% SL, 33% Hyperplastic Polyps (HP), 34% tubular adenoma (TA)). Histopathological diagnosis of the polyps was used as the gold standard for comparison. Presentation of polyps (SSL, TA, HP) was randomized so that 50% of the polyps seen were presented with displaying a CADx diagnosis for the polyp and 50% were presented without displaying a CADx diagnosis.

Results

A total of 1955 optical polyp diagnoses were performed. The optical diagnostic accuracy amongst various endoscopists to recognize SSLs was: expert gastroenterologists (52.3%, 95% Confidence Interval (CI) 46.3-58.3), trainee gastroenterologists (38.2%, CI 27.2-49.1), expert surgeons (22.1%, CI 16.2-28.0) and trainee surgeons (14.9%, CI 8.4-21.5). Further details are provided in Table 1. CADx identified 52.6% of SSL as HP and 47.4% as TA. CADx identified 60.5% of HP correctly and 82.1% of TA correctly. The optical diagnostic accuracy for HP and TA improved among endoscopists when a correct CADx diagnosis was displayed to the endoscopists compared to when no CADx information was displayed (HP: 63.7% vs 38.4% / TA: 81.5% vs 70.5%; p=0.05). The optical diagnostic accuracy of the endoscopists worsened when the displayed CADx diagnosis was incorrect (HP: 24% / TA: 25%). The diagnostic accuracy of the endoscopists for SSL did not differ significantly when the CADx diagnosis, which is currently untrained to diagnose SSL and can only classify polyps as hyperplastic or adenomatous, was displayed (37.2% vs 33.1%, p=0.05).

	Sessile Serrated Lesion	Hyperplastic Polyp	Adenoma
CADx Alone	HP: 52.6 (36.8-68.5) TA: 47.4 (31.5-63.2)	Sn 60.5 (49.5-71.5) Sp 82.1 (70.0-94.1) PPV 86.8 (77.7-95.9) NPV 51.6 (39.2-64.1)	Sn 82.1 (70.0-94.1) Sp 60.5 (49.5-71.5) PPV 51.6 (39.2-64.1) NPV 86.8 (77.7-95.9)
CADx diagnosis NOT displayed All Endoscopists (n=17)	Sn 37.2 (31.9-42.4) Sp 78.6 (75.5-81.8) PPV 46.2 (40.1-52.2) NPV 71.7 (68.4-75.0)	Sn 38.4 (33.1-43.7) Sp 79.8 (76.8-82.9) PPV 48.4 (42.3-54.6) NPV 72.4 (69.2-75.7)	Sn 70.5 (65.6-75.4) Sp 64.7 (61.0-68.4) PPV 50.6 (46.1-55.2) NPV 81.0 (77.6-84.4)
CADx Displayed Correct Diagnosis All Endoscopists (n=17)	Sn 33.1 (28.0-38.3) Sp 79.4 (76.3-82.5) PPV 44.2 (38.0-50.5) NPV 70.6 (67.3-73.9)	Sn 63.7 (57.4-70.0) Sp 78.7 (74.9-82.5) PPV 60.2 (53.9-66.4) NPV 81.1 (77.4-84.8)	Sn 81.5 (76.9-86.2) Sp 86.5 (83.2-89.9) PPV 80.7 (76.0-85.3) NPV 87.2 (83.9-90.5)
CADx Displayed Incorrect Diagnosis All Endoscopists (n=17)		Sn 24.0 (15.6-32.4) Sp 77.4 (71.7-83.0) PPV 33.3 (22.4-44.2) NPV 68.3 (62.4-74.2)	Sn 25.0 (14.0-36.0) Sp 45.2 (39.1-51.4) PPV 9.8 (5.1-14.5) NPV 71.7 (64.7-78.7)
Breakdown of the performance by endoscopists category GE: Gastroenterologist Expert (n=7) GT: Gastroenterologist Trainee (n=2) SE: Surgery Expert (n=5) ST: Surgery Trainee (n=3)
Overall	GE: 52.3 (46.3-58.3) GT: 38.2 (27.2-49.1) SE: 22.1 (16.2-28.0) ST: 14.9 (8.4-21.5)	GE: 35.0 (29.2-40.7) GT: 61.8 (50.9-72.8) SE: 53.7 (46.6-60.8) ST: 42.1 (33.0-51.2)	GE: 74.7 (69.6-79.9) GT: 64.1 (53.5-74.7) SE: 66.2 (59.5-72.8) ST: 74.4 (66.4-82.3)
CADx diagnosis NOT displayed	GE: 54.1 (45.7-62.6) GT: 39.5 (23.9-55.0) SE: 23.2 (14.7-31.6) ST: 19.3 (9.1-29.5)	GE: 27.8 (20.2-35.4) GT: 57.9 (42.2-73.6) SE: 53.7 (43.7-63.7) ST: 24.6 (13.4-35.7)	GE: 72.8 (65.3-80.3) GT: 66.7 (51.9-81.5) SE: 70.4 (61.4-79.4) ST: 69.5 (57.7-81.2)
CADx Displayed Correct Diagnosis	GE: 50.4 (41.9-58.9) GT: 36.8 (21.5-52.2) SE: 21.1 (12.9-29.3) ST: 10.5 (2.6-18.5)	GE: 50.6 (40.2-60.9) GT: 84.6 (70.7-98.5) SE: 67.2 (55.9-78.4) ST: 73.2 (59.6-86.7)	GE: 85.0 (78.4-91.5) GT: 75.0 (60.0-90.0) SE: 73.4 (63.7-83.2) ST: 91.5 (83.5-99.5)
CADx Displayed Incorrect Diagnosis		GE: 25.0 (12.2-37.8) GT: 25.0 (0.5-49.5) SE: 21.4 (6.2-36.6) ST: 25.0 (3.8-46.2)	GE: 37.5 (18.1-56.9) GT: 0.0 (0.0-0.0) SE: 16.7 (0.0-33.9) ST: 27.3 (1.0-53.6)

Table 1. CADx and endoscopists performance in optical diagnosis of SSL, HP and adenomas depending on the polyp histology in percentages of correct optical diagnoses with 95% confidence intervals. For SSL, CADx is not able to provide correct diagnosis; thus is presented as CADx shown as either HP or TA)
Sn: sensitivity; Sp: specificity; PPV: positive predictive value; NPV: negative predictive value

Conclusion

Optical polyp diagnosis that includes SSL, TA, and HP recognition remains challenging. Current CADx systems when displaying correct diagnostic outputs improve diagnostic accuracy of the endoscopists. CADx systems not trained for SSL recognition do not significantly affect SSL recognition among endoscopists even if an incorrect diagnostic output (TA or HP) is displayed. Development of improved CADx systems with SSL recognition and higher diagnostic accuracy is warranted.

Disclosure

Dr Jeffrey Marks: Consultant for Boston Scientific and Steris Endoscopy

Introduction

Recent investigations have highlighted specific dimensions within the bony pelvis as potential indicators of total mesorectal excision (TME) outcomes. However, manual pelvimetry is labor-intensive.

Aims & Methods

This study endeavors to develop an automated pelvimetry system employing artificial intelligence (AI) to facilitate the preoperative identification of high-risk patients. Specifically, our research aims to construct and validate an AI-based automated pelvimetry tool and assess the prognostic utility of pelvimetric measurements in predicting postoperative complications necessitating reintervention (Clavien Dindo classification ≥ 3) in patients undergoing minimally invasive TME. This study is a multicenter retrospective cohort investigation incorporating clinical and imaging data from 1,700 patients treated at eight experienced TME centers. Leveraging a Deep-Learning network, our algorithm locates multiple anatomical landmarks to quantify pelvic inlet, pelvic outlet, and depth distances accurately. The accuracy of the developed algorithm is compared against inter-observer variability observed in manually conducted pelvimetry. The prognostic significance of pelvimetry for (post-)operative outcomes is evaluated through multivariate regression analyses.

Results

The AI-driven landmark localization achieved a mean margin of error of 5 millimeters across various pelvic dimensions, while manual pelvimetry demonstrated an inter-observer variation of 2.5 millimeters. Analyses of pelvimetric measurements and postoperative complications identified pelvic depth as an independent predictor of such complications (p < 0.001).

Conclusion

An artificial intelligence algorithm could successfully be used for accurate automated pelvic dimension determination. These pelvimetric measurements hold promise as a valuable decision support tool for surgeons in preoperatively assessing the risk of postoperative complications following minimally invasive TME.

Introduction

The depth of early gastric cancer is an important factor in determining the treatment plan such as endoscopic resection or surgery. We developed an artificial intelligence (AI) program using deep neural networks to diagnose mucosal and submucosal cancers using endoscopic images. Performance evaluation tests were conducted for clinical applications.

Aims & Methods

A validation dataset was established comprising 204 patients with early gastric cancer who underwent endoscopic resection or surgery at our hospital during 2018–2021. The lesion sites were the upper part in 16 (8%), middle part in 106 (52%), and lower part in 82 (40%) patients. The median lesion size (range) was 14 (2–90) mm; the lesion depth was mucosal cancer in 136 (67%) and submucosal cancer in 68 (33%) patients. Five white-light images per case at a minimum were used (median: 8, range: 5–22). The primary endpoint was sensitivity of mucosal cancer, defined as the lower limit of the 95% confidence interval (CI) > 75%, based on previous reports on the depth diagnosis of early gastric cancer. The secondary endpoints were specificity and accuracy.

Results

Sensitivity of this program for the diagnosis of mucosal cancer was 91.2% (95% CI: 85.2%–94.9%), indicating the usefulness of this program. Specificity was 66.2% (95% CI: 54.3%–76.3%), and accuracy was 82.8% (95% CI: 77.1%–87.4%).

Conclusion

The AI program developed for diagnosing the depth of early gastric cancer is useful and expected to be applied clinically in the near future.

Introduction

Potential celiac disease (PCD) is a clinical condition characterized by the presence of a positive CD-specific serology and a normal intestinal architecture. Asymptomatic PCD patients are generally addressed to continue on a gluten containing diet (GCD), but long-term risks of this approach have never been explored.

Aims & Methods

In the present study we aim at investigating nutritional and autoimmune complications possibly developing overtime in a cohort of asymptomatic PCD children left on a GCD. We compared parameters of growth, nutritional status, and autoimmunity between time of diagnosis and in occasion of last medical check, after a long-term gluten containing diet.

Results

Altogether we collected data from 171 PCD children with a mean follow-up time of 3 years (range 0.35-15.3 years). During follow-up, despite patients did not reduce the amount of daily gluten intake , anti-tissue transglutaminase (anti-TG2) antibodies spontaneously and significantly decreased. Most of parameters analysed did not change over follow-up (height centile, ferritin, albumin., cholesterol, calcium, alkaline phosphatase , parathormone, vitamin D, p>0.05) or even improved (weight and BMI centile, haemoglobin, blood iron, HDL, glycaemia and HbA1C, p<0.05), always remaining in the limit of normality. Equally autoantibodies for other concomitant autoimmune disorders did not increase overtime. Similar results were obtained excluding from the analysis patients that stopped producing anti-TG2 and those with a follow up < 3 years.

Conclusion

Our pilot study provides reassuring results on the maintenance of a gluten containing diet in asymptomatic PCD children, even when a long term follow-up is considered.

Disclosure

no conflict of interest to declare

Introduction

Pancreatic ductal adenocarcinoma (PDAC) is a lethal disease with the lowest 5-year survival rate of all cancers due to late diagnosis and lack of effective treatment. One of the challenges of developing molecular diagnosis and molecular targeted therapy for PDAC is the “mismatch” between research models and patients.

Aims & Methods

This study aimed i) to identify common proteins among different research models and patients and accordingly ii) to identify potential hub proteins and signaling pathways involved in the PDAC pathogenesis by using proteomics-based system modeling of multimodal datasets derived from multiple research models and patients. Mouse PDAC cells (derived from a genetically engineered mouse model with KrasG12D/Pdx1-Cre), PDAC orthotopically tumors in C57B/L6 mice (n=5), mouse PDAC spheroids, mouse pancreatic organoids, mouse PDAC organoids, normal mouse pancreatic tissues (n=5), human PDAC tumor tissue samples from treatment-naїve (n=20), neoadjuvant chemotherapy (n=22), and human PDAC organoids were included. Mass spectrometry-based proteomics, Rstudio, Cytoscape 3.10 and pathway enrichment analysis (using ingenuity pathways analysis, including human PDAC transcriptomic in addition) were used.

Results

In the comparison of PDAC models and patients, shared proteins were identified as follows: 60% between the mouse PDAC cells and the mouse PDAC spheroids, 78% between the mouse PDAC cells and the mouse PDAC tissue, 56% between the mouse PDAC spheroids and the mouse PDAC tissue, 66% between the mouse PDAC cells and the mouse PDAC organoids, 54% between the mouse PDAC spheroids and the mouse PDAC organoids, 38% between the mouse PDAC and the human PDAC tissues, 31% between the human PDAC tumor and the human PDAC organoids, and 60% between the mouse and the human PDAC organoids. A total of 1,367 common proteins were identified within all the PDAC models. Among these proteins ACTB, HSP90AA1, HSPA8, HSP90AB1, HSPA4, EEF2, RPS3, RPS20 and GAPDH were the top 10 hub proteins (with >300 node connections). Upon comparison of all eight models, eight proteins were identified as unique to PDAC, namely FAHD1, ARSA, NEK9, SERPINB9, LAMTOR2, SNRPG, TMED3, and RAB1A. There was a correlation in terms of z-score regarding signaling pathways between the mouse PDAC and human PDAC (p<0.001), particularly in activations of nerve growth factor signaling, integrin signaling, and neuregulin signaling. Central hub proteins commonly found in the mouse models included vinculin (membrane-cytoskeletal protein) as one of the high connectivity nodes and β-catenin and probable ubiquitin carboxyl-terminal hydrolase FAF-X (involved in axonal growth and neuronal cell migration) as high centrality nodes. In patients, central hub proteins that were dysregulated after chemotherapy included 14-3-3 protein zeta/delta (encoded by YWHAZ gene), β-catenin and SRC. Among the 47 proteins that were associated with altered metabolism after the chemotherapy in patients, CD44, CTNND1, APOA 1, PARVA, and SCRBS1 were nodes with low degrees, and desmosomes (DSP, KP3, PPL), PRTN3, thrombospondin-2 (THBS2), and DMBT1 were the most down-regulated proteins without nodes, and APOA1, caveolin-1 (encoded by CAV1), LRP1 were the most up-regulated proteins with nodes.

Conclusion

The system modeling identified the common proteins among the research models and patients and the hub proteins and signaling pathways, suggesting the potential translational targets of PDAC.

Introduction

Dysregulated T-cell immune response-mediated inflammation is implicated in various liver diseases, yet mechanisms governing liver immune homeostasis control and therapies for limiting T-cell overactivation remain unclear. This study aims to elucidate the role of immunoregulatory myeloid cells (MCregs) in liver injury and their potential to modulate CD4⁺ T-cell hyperactivation via extracellular vesicle (EV)-mediated metabolic regulation.

Aims & Methods

Concanavalin A (ConA) mice and autoimmune hepatitis (AIH) patients were examined for alterations in purine metabolism associated with liver injury severity and therapeutic response. The expression of extracellular purine catabolism nucleases, such as CD39 and CD73, on CD11b⁺Gr-1⁺ myeloid cells (MCs) was assessed, with particular focus on liver injury conditions. The study investigated the natural expansion of the CD39⁺CD73⁺Gr-1^highCD11b⁺ regulatory MC (MCreg) subset during early liver injury phases and its effects on CD4⁺ T-cell hyperactivation and liver injury through in vitro and in vivo experiments. Mechanistic insights into MCreg-mediated immunosuppression were explored, particularly regarding the release of CD73^high EVs and their impact on extracellular AMP conversion, cAMP pathway activation, and inhibition of glycolysis and cytokine secretion in activated CD4⁺ T cells.

Results

Alterations in purine metabolism were observed in ConA mice and AIH patients, correlating with liver injury severity and therapeutic response. CD39 and CD73 expression was markedly upregulated on CD11b⁺Gr-1⁺ myeloid cells under liver injury conditions. The CD39⁺CD73⁺Gr-1^highCD11b⁺ MCreg subset expanded naturally during early-stage liver injury and demonstrated efficacy in suppressing CD4⁺ T-cell hyperactivation and liver injury both in vitro and in vivo. The release of CD73^high EVs by MCregs facilitated extracellular AMP conversion to immunosuppressive metabolites (e.g., adenosine and inosine), activating the cAMP pathway and inhibiting glycolysis and cytokine secretion in activated CD4⁺ T cells.

Conclusion

These findings shed light on the mechanisms governing immune homeostasis during early liver injury and suggest that MCreg or MCreg-EV therapy may offer a targeted approach for preventing diverse liver diseases induced by T-cell overactivation.