Educational objectives:
1. To give an overview of the main cutaneous manifestation of IBD 
2. To understand the importance of the clinico-pathological correlation for their diagnosis 

Crohn’s disease (CD) is a lifelong chronic illness with recurrent relapsing and remitting disease course that requires close follow ups and reassessments of disease status as well as screening for complications throughout a patient’s lifetime. Imaging plays a crucial role in the diagnosis and evaluation of CD. Currently, different imaging modalities can be used over the disease course of a patient’s lifetime, from the diagnosis of the disease, to determining the extent of intestinal involvement, monitoring for disease activity, and evaluating for CD related complications. Intestinal Ultrasound (IUS) is a non-invasive, radiation-free, and safe useful imaging tool that can be used in the diagnosis and management of crohn’s disease. 

Educational Objectives:

1. To emphasize on the increased rule of imaging in IBD management.
2. To review the rule of Intestinal ultrasound (IUS) in detecting Crohn’s disease complications:
   1. Strictures.
   2. Abdominal fistulas.
   3. Inflammatory Masses.
3. To review the latest guidelines statements for the use of intestinal Ultrasound in detecting crohn’s disease complications.
4. Quick overview on the innovated tools to improve the accuracy of intestinal ultrasound in detecting crohn’s disease complications.

Interactive cases will be presented to illustrate IUS diagnosis of UC complications, such as acute severe colitis, including monitoring of response to treatment, perforation, chronic structural damage in UC, infectious colitis, pseudopolips and lymphoma.

Background

Capsule endoscopy (CE) is the gold-standard for the evaluation of the enteric mucosa in patients with suspected or known inflammatory bowel disease, particularly Crohn’s disease. Ulcers and erosions of the small bowel are common findings and their identification in CE is paramount for an accurate disease stratification.

Several artificial intelligence (AI) algorithms have been developed to aid endoscopists to detect lesions in different endoscopic modalities. With this project we intend to develop and test an AI algorithm for the automatic identification of ulcers and erosions in the small bowel mucosa.

Methods

A total of 2565 CE exams from two different centers (1483 from São João University Hospital and 1082 from ManopH Gastroenterology Clinic) were used to develop the Convolutional Neural Network (CNN). 55320 frames of the enteric mucosa were obtained, 18396 containing enteric ulcers and erosions, and 36924 containing normal mucosa. 90% of the frames were used to develop the training dataset and 10% were used to test the network. The patients included on the training dataset were excluded from the testing dataset. This patient split brings the technology performance closer to that of a real-life setting. The output provided by the CNN was compared to the classification provided by a consensus of experts.

Results

Our model was able to automatically detect ulcers and erosions in the enteric mucosa with an accuracy of 93.2%, sensitivity of 90.4% and a specificity of 93.9%. The mean processing time for the validation dataset was 29 seconds (approximately 306 frames/second).
An example of the output obtained after the network application can be seen in Figure 1.

Conclusion

The authors developed a CNN for the automatic identification of enteric ulcers and erosions in CE videos and tested it in AI naïve patients. This represents an evolution in the technology readiness level into a real-life clinical setting, that will surely improve the diagnostic yield of CE exams, which will ultimately translate into better patient care.

Early prediction of intravenous corticosteroid (IVCS) resistance in Acute severe ulcerative colitis (ASUC) patients could reduce costs and delay in rescue therapy. However, most prediction models for ASUC were at high risk of bias with a lack of external validation. This study aims to construct and validate a model that accurately predicts IVCS resistance using various statistical methods.

A retrospective cohort of patients who were diagnosed with ASUC and had undergone IVCS treatment between March 2012 to January 2020 was established. Predictors evaluated included age, gender, race, medications before admission, infections, and laboratory data at baseline and during IVCS treatment, and endoscopic outcomes relied on blinded centralized endoscopy reading. The LASSO regression was used in feature selection for multivariate logistic regression model. Models based on machine learning methods (decision tree and random forest [RF]) were also constructed. Internal validity was confirmed and model performances were compared. External validation was conducted using data using an independent cohort from a tertiary referral centre.

A total of 129 patients were included in the derivation cohort. During index hospitalization, 102 (79.1%) responded to IVCS, and 27 (20.9%) failed; 16 patients underwent colectomy, 6 received cyclosporin, and 5 succeeded with IFX as rescue therapy. Ulcerative Colitis Endoscopic Index of Severity (UCEIS; odds ratio [OR] 5.39, 95% confidence interval [CI] 2.52-14.0, p<0.001) and C-reactive protein (CRP) level on the third day (OR 1.05, 95% CI 1.03-1.08, p<0.001) were selected by LASSO regression and identified as the only two independent predictors of IVCS resistance in logistic regression. The decision tree model identified a UCEIS higher than 6.5 points and CRP level at day 3 higher than 33.57 mg/dL as the proxy for IVCS resistance. UCEIS and CRP level at day 3 were also the most important predictors in the RF model. Areas under the curve receiver operating characteristic (AUC) of logistic model, decision tree model, and RF model were 0.64 (95% CI 0.49-0.80), 0.81 (95% CI 0.71-0.90), and 0.88 (95% CI 0.82-0.95), respectively. A validation cohort of 65 ASUC patients were established, and the AUC of the models in external validation were 0.57 (95% CI 0.45-0.70), 0.70 (95% CI 0.61-0.80), and 0.71 (95% CI 0.48-0.94), respectively.

In patients with ASUC, UCEIS and CRP level at day 3 of IVCS treatment appeared to allow the prompt prediction of likely IVCS nonresponders. Machine learning-based models outperformed the traditional statistical model in the prediction. The models may aid therapeutic decision-making in ASUC patients.

In developing a patient-reported outcome (PRO) for pediatric ulcerative colitis (UC) with guidance from FDA and EMA, 8 items were previously selected based on 79 concept elicitation interviews. An observer RO (ObsRO) was determined to be required for children younger than 8 years. Here, we aimed to finalize the included items and to validate the TUMMY-UC for its psychometric properties.

The structure and exact wording of the PRO and the ObsRO versions were determined by cognitive debriefing interviews with children and their caregivers. Weights were assigned to each item based on ranking of importance. Then, in a prospective multicenter study, children with UC between 2-18 years who either underwent colonoscopy or provided stool for calprotectin completed the TUMMY-UC during 4 consecutive days, as well as 7 and 21 days thereafter for evaluating reliability and responsiveness. Construct and discriminative validity were assessed by different measures of disease severity and quality of life (QOL).  

In an iterative process of 129 cognitive interviews, the exact wording of the TUMMY-UC was determined. The PRO and ObsRO were formatted with identical structure to ensure conceptual equivalence for incorporating into one score. 71 children were included in the validation study (39 with colonoscopy and 32 with calprotectin; age 12.3±4.1 years, 26 (36%) in remission, 20 (29%) with moderate-severe disease). There was excellent reliability in the repeated day assessments (ICC 0.93 (0.88-0.96); p<0.001) and after 1 week in those judged as unchanged (0.90 (0.81-0.95); p<0.001). The TUMMY-UC total score had moderate to strong correlations with all constructs of disease severity: r=0.64 with UC Endoscopic Index of Severity (UCEIS, Figure 1), r=0.66 with IMPACT QOL questionnaire, r=0.43 with calprotectin, r=0.82 with the PUCAI, r=0.76 with patient/caregiver global assessment, r=0.5 with CRP, and r=-0.36 with albumin (all p<0.015). There was a slight superiority to combining TUMMY-UC scores of two consecutive days. The index had excellent discrimination of disease activity categories (figure 2) with a score<9 defining remission (Sen=93%, Spec=84%, AUROC=0.95 (95%CI 0.89-0.99). Showing high responsiveness, the DTUMMY-UC differentiated well between children who improved, worsened or remained unchanged after 3 weeks (Figure 3).The best cutoff of the TUMMY-UC to define response was a change of ≥10 points (AUROC 0.93 (95%CI 0.86-0.99)).

The TUMMY-UC, constructed from a PRO and ObsRO versions for children 8-18 and 2-7 years, respectively, is a reliable, valid and responsive index which can be now used in clinical practice and as an outcome measure in clinical trials.

Humans are colonized by complex microbial communities which contribute to physiological processes in the host. The communication between microbes and host is crucial to maintain the homeostasis and gut health. Disruption in the microbiome composition leads to increased inflammation and appearance of diseases, such as inflammatory bowel disease (IBD). Interspecies interaction prediction combined with gene expression patterns on individual cell level by single-cell omics data reveals a new insight into the molecular background of cell-type specific host-microbe interactions.

Previously we developed the MicrobioLink pipeline (Andrighetti et al, Cells, 2020), an in silico microbe-host protein-protein interaction prediction algorithm. Here, we implemented a computational workflow based on MicrobioLink to predict and compare the cell-specific effects of a commensal bacteria in healthy and diseased conditions using a publicly available single-cell RNAseq dataset (Smilie et al, Cell, 2019) from colon biopsies describing 51 cell types  - including fibroblasts, epithelial and immune cells - in healthy, non-inflamed and inflamed ulcerative colitis (UC). With functional analysis, microbe-affected processes have been discovered, while reliable network biology resources, such as OmniPath and Reactome, were used to identify the direct mechanism of action of the bacterial molecules.

Demonstrating the applicability of the new computational workflow, we analysed the effect of a common gut commensal bacteria - Bacteroides thetaiotaomicron (Bt) - on human immune cells focusing on the Toll-like receptor (TLR) signalling. We found that extracellular vesicles (EVs) secreted by Bt may be able to modulate the TLR pathway intracellularly. The analysis highlighted that Bt targets differ among cells and between the same cells in healthy versus UC conditions. The in silico findings were validated in EV-monocyte co-cultures demonstrating the requirement for TLR4 and Toll-interleukin-1 receptor domain-containing adaptor protein (TIRAP) in EV-elicited NF-kB activation.

The current pipeline offers potentially interesting connection points and detailed mechanistic insight containing mechanistic information about microbe-host interactions. This information can be tested and harnessed to understand better how microbial proteins may be of therapeutic value in inflammatory diseases, such as IBD.

Previous studies have described machine learning (ML) models to predict how human readers would score disease activity in UC using the endoscopic Mayo Score (eMS). So far, none employed deep human annotation that considers all the endoscopic features making up the eMS.  Here we report the results of an ML model that is trained on eMS features using centrally read endoscopies.

793 full-length videos were obtained from 249 patients with UC who participated in NCT02589665, a phase 2 trial with mirikizumab in patients with UC and associated with centrally read (single reader) eMS (CReMS) as the primary dataset. After cleaning for usable frames, the data were split into training, validation and testing subsets. The ML workflow consisted of annotation, segmentation, and classification (e.g., erosions, ulcers, erythema, vascular pattern, and bleeding). Human image classification and segmentation with bounding boxes and was subjected to quality control adjudicated by one of three IBD specialists, generating more than 60,000 eMS-relevant annotation labels. The model was evaluated on a test set of 147 videos using the CReMS, and a consensus set of 94 test videos, where CReMS and annotator reported eMS (AReMS) were in agreement without adjudication. The primary objective of the model was a categorical prediction of endoscopically inactive disease (eMS 0 & 1) compared with active disease (eMS 2 & 3). The secondary objectives of the model were to predict endoscopic healing (eMS 0) and to predict severe disease (eMS 3).

The model performances are in Table 1. On the full test set of 147 videos, the model predicted inactive disease compared with active disease with an accuracy of 84%, positive predictive value (PPV) of 80%, and negative predictive value (NPV) of 85%. In the subset of 94 videos with CreMS and AReMS consensus, the model predicted inactive disease compared with active disease with an accuracy of 89%, PPV 87%, and NPV of 90%. In this same subset, the model predicted endoscopic healing and severe disease with an accuracy of 95% and 85%, PPVs of 86% and 82% and NPVs of 95% and 87%, respectively. For the secondary objectives in the full set of 147 videos, the model predicted endoscopic healing and severe disease with an accuracy of 90% and 80%, PPVs of 44% and 86%, and NPVs of 95% and 86%, respectively.

We have developed a ML predictive model of the eMS in UC using centrally read videos and demonstrate excellent distinction between active and inactive disease, and clear discrimination between other levels of endoscopic activity. We propose that this unique ML approach to endoscopic assessment be considered as a substitute to human central reading in future clinical trials.

1) Crohn’s disease and ulcerative colitis are distinct diseases but have many aspects in common, probably they just represent the extreme ends of the IBD spectrum
2) The ethiopathogenesis of IBD is far from completely unravelled, but there seems to be an important interplay between genetic, environmental and immunological factors
3) Diagnostic modalities consist of a good clinical history, clinical examination, biological tests, radiological and endoscopic imaging, and histological examination

Crohn Disease (CD) prevalence is rising worldwide. As altered genetics are improbable, this phenomenon likely relates to environmental-dietary changes linked with gut microbiome. We aimed to define host and microbial factors at CD diagnosis.

Multiomics analyses ofSOURCE cohort including clinical, biomarkers (CRP, fecal calprotectin), food frequency questionnaire (FFQ), serum metabolomics, mucosal terminal ileum (TI) transcriptomics, and fecal and mucosal biopsy samples for 16S microbial amplicon sequencing.

25 newly-diagnosed CD and 33 controls (median age 28 years, 50% males). Gender, age, and BMI did not differ between groups, but CRP (p=0.001) and calprotectin (p=E-10) were significantly higher in CD. FFQ results showed that compared to controls, pre-diagnosis CD patients consumed significantly more added sugar (g/day), starch (g/day) nitrite (mg/day), and significantly less vitamin K, D, vegetables, and olive oil (Fig 1). Microbial analyses highlighted significant differences (FDR<0.1) in amplicon sequence variants (ASVs) abundance between stool and biopsies samples (73 ASVs) and between CD and Controls samples (82 ASVs). Biopsy vs. stool samples were enriched for Veillonella, Fusobacterium, Neisseria, and Ruminococcus gnavus. CD showed higher abundance of Enterobacteriaceae and Ruminococcus gnavus with reduction of several Ruminococcaceae and Lachnospiraceae taxa (Fig. 1). Ileal transcriptomics differential expression (FC>1.5, FDR<0.05) replicated previous results with significant induction in CD of DUOX2, CXCL9, and DEFB4A and pathways linked to innate and adaptive immunity, and to extracellular matrix. CD down regulated genes included GUCA2B, SLC10A2, and GSTA1, and pathways linked with epithelial transporters. Serum metabolomics highlighted significant variations (FDR<0.25) in  Linoleic acid, aKG, Tryptophan, nicotinamide, Docosahexaenoic acid, oxalate, and GABA between CD and controls. We next tested for significant association (FDR<0.25) between diet and multi-OMICs (Fig. 2). Associations between gut microbiome and TI transcriptomics, and serum metabolomics showed that Erysipelotrichacea taxa positivity correlated with serum oxalate, and TI expression of CEACAM6 and DUOX2. Associations between diet and TI transcriptomics and serum metabolomics indicated that B12, tryptophan and riboflavin consumptions were negatively associated with the bile acid transporter SLC10A2 in the TI, and vegetables consumption was positively associated with oxalate degrading Oxalobacter.

Conclusions: FFQ identifies difference in diet at the onset of CD that may contribute to pathogenesis. Integration between dietary and OMICs layers disclosed novel correlations warranting further exploration.

Objectives:
1. To understand the role of the IBD dietitian in the MDT 
2. To review the current dietary approaches for IBD management
3. To discuss whether diet can be used as a prevention strategy

Understand whether upper GI pathology is found in UC, and byanswering the following questions:

1.      Do patients with ulcerative colitis have lesions in the upper GI tract?
2.     Are there lesions in the upper GI tract that are unique to ulcerative colitis? 
3.    If upper GI UC exist, are there any predisposing factors?
4.    Is it possible to use upper GI biopsies in patients with colitis, to distinguish UC from Crohn’s disease (CD)?

Inflammatory bowel disease specimens have specific features which, in general, differ between Ulcerative Colitis and Crohn's disease.
The typical characteristics of UC are diffuse mucosal alteration and a regular bowel wall, whereas CD specimens usually show thickening of the bowel wall, visceral adhesions and mucosal skip lesion. 
It is important to identify and recognise these features in order to section the surgical organs correctly. For this reason, there are recommendations for the cut up of IBD specimens. 

Educational objectives:
- To learn how to approach an IBD specimen
- To understand the macroscopic differences between UC and CD specimens
- To learn how to dissect an IBD specimen and why
- To understand the importance of cut up in IBD pathology

1. To define the appropriate timing between medical and surgical management of IBD
2. To review medical and surgical treatment indications of the complications of IBD  
3. To learn how to decide in multidisciplinary team between the two modalities of treatment 

Discussion of presented topics as a chair of the complete session

Discussion of Tailored medical therapy in UC and the surgical approach.

Ulcerative proctitis (UP), defined as a colonic location limited to the rectum, is a poorly investigated condition in children, usually considered as a minor form of Ulcerative Colitis (UC). The aim of the present study was to compare the disease course of paediatric patients affected by UP at diagnosis with the other UC locations.

This multicentre retrospective observational study has been carried out starting from the data prospectively registered in the IBD Registry of the Italian Society of Pediatric Gastroenterology, Hepatology and Nutrition (SIGENP). Seventeen IBD referral centres adhering to the registry were included in the study. Patients age 0 to 18 years, who were diagnosed with UC according to the Porto criteria starting from January 1, 2009, to May 1st, 2021 were identified. Only children with a minimum follow-up of 12 months were included in the study. Once enrolled, children were subsequently divided in two groups based on Paris classification: group 1 (E1) and group 2 (E2, E3 and E4).

Eight-hundred-eighty-five children were finally included in the study (median age at diagnosis: 11.2 years, range: 0-18 years; M/F: 434/451), of whom 176 (19.8%) belonging to group 1 and 709 (80.1%) to group 2. The median age at diagnosis was significantly higher in group 1 when compared to group 2 [11.9 (0-18) versus 11 (0-18) years, respectively; (p<0.001)]. At diagnosis, the induction therapy was significantly different with 68 (39.5%) patients of group 1 undergoing steroid therapy versus 505 (71.2%) of group 2 (p<0.001) and 79 (41.9%) of group 1 practising only mesalamine respect to 186 (26.2%) of group 2 (p<0.001).  A higher number of children from group 2 started immunosuppressive or biologic therapy as maintenance therapy at diagnosis [Group 1: 11 (6.2%) versus 173 (24.4%), respectively; (p<0.001)]. The median follow-up of our cohort was 4.5 years (range 1-13 years). At the last follow-up, 67/176 (38%) children with UP showed an extension of their disease location without significant difference when compared to group 2 [265 (37.5%); p=0.9], while 81 (45%) children from Group 1 were under immunosuppressive or biologic therapy versus 566 (79.8%) from group 2 (p<0.001). Five children (3%) of Group 1 underwent colectomy during the follow up versus 45 (6.9%) of Group 2 (p=0.06).

UP is a frequent location of paediatric onset UC and the risk of endoscopic extension of proctitis is similar to the more extensive forms. A considerable number of patients with UP required immunosuppressive or biologic therapy during the follow-up and no significant difference was observed in terms of surgery. Overall, UP cannot be considered as a minor form of UC.

In recent decades, there has been a growing appreciation that Inflammatory Bowel Disease (IBD) needs a personalized approach to treatment so that the right therapy can be given to the right patient at the right time. Indeed, the potential benefits of personalized medicine for IBD are evident. One approach may be through prognostic information, which is essential for clinical decision-making, as it provides physicians with substantial evidence that can assist them in guiding patients during their disease and treatment course. Prognostic markers, such as clinical, serological, endoscopy, fecal, and histology factors, predict the natural course of the disease, and their utility is based on the assumption that treatment stratification can impact the course of the disease. Therefore, recent literature has focused on identifying good prognostic models based on genetic/serological or clinical/demographic factors that could substantially contribute to tailoring each patient's treatment and potentially surpass a significant barrier to personalized medicine in IBD due to the lack of algorithms to guide treatment from diagnosis.

This talk will present and summarize the most recent scientific evidence of disease risk factors as prognostic tools in Crohn's disease (CD) and ulcerative colitis (UC) and provide some take-home messages on this topic such as:

-        Patients with CD and UC are so heterogeneous that a single treatment algorithm will never be suitable. Therefore, we need individualized treatment options and decisions.

-        System dynamics analysis is a methodology that addresses the inherent dynamic complexity of interactions between variables. As an alternative to traditional statistical methods, it may have the ability to translate complex clinical data into patient-friendly results.

-        Bayesian networks can be seen as an alternative to logistic regression, where statistical dependence and independence are not hidden in approximating weights but rather explicitly represented by links in a network of variables.