Primary small bowel follicular lymphoma: the role of balloon-assisted enteroscopy in diagnosis and follow-up.

An asymptomatic 38-year-old male with no significant previous medical history performed routine laboratory studies that revealed iron-deficiency anemia. Esophagogastroduodenoscopy and colonoscopy were unremarkable and he undergone videocapsule endoscopy that revealed multiple small polyps along jejunum and ileum. Double-balloon enteroscopy confirmed the presence of scattered small whitish nodules and small polyps carpeting segments of jejunal mucosal and sometimes forming conglomerates with a nodular appearance. Histopathological examination showed lamina propria expansion by neoplastic follicles, predominantly composed by small lymphoid cells that, by immunohistochemistry, showed expression of CD20, CD10 and bcl-2. Computed tomography scan of abdomen and pelvis did not reveal systemic involvement, consistent with primary small bowel follicular lymphoma. Chemotherapy was started and, at reevaluation enteroscopy, although nodular jejunal segments persisted, biopsies did not show involvement by lymphoproliferative disease, which was interpreted as complete remission. Periodic clinical and biochemical evaluation and annual enteroscopic surveillance was maintained and, after three years, local recurrence of low-grade follicular lymphoma was detected. As previously, there was no evidence of systemic involvement and the decision was to maintain close surveillance. After one year, the patient remains asymptomatic and without evidence of disease progression. This case illustrates the essential role of balloon-assisted enteroscopy for diagnosis and surveillance of primary small bowel follicular lymphoma.

Artificial intelligence and capsule endoscopy: automatic detection of enteric protruding lesions using a convolutional neural network.

BACKGROUND AND AIMS: capsule endoscopy (CE) revolutionized the study of the small intestine. Nevertheless, reviewing CE images is time-consuming and prone to error. Artificial intelligence algorithms, particularly convolutional neural networks (CNN), are expected to overcome these drawbacks. Protruding lesions of the small intestine exhibit enormous morphological diversity in CE images. This study aimed to develop a CNN-based algorithm for the automatic detection small bowel protruding lesions. METHODS: a CNN was developed using a pool of CE images containing protruding lesions or normal mucosa from 1,229 patients. A training dataset was used for the development of the model. The performance of the network was evaluated using an independent dataset, by calculating its sensitivity, specificity, accuracy, positive and negative predictive values. RESULTS: a total of 18,625 CE images (2,830 showing protruding lesions and 15,795 normal mucosa) were included. Training and validation datasets were built with an 80 %/20 % distribution, respectively. After optimizing the architecture of the network, our model automatically detected small-bowel protruding lesions with an accuracy of 92.5 %. CNN had a sensitivity and specificity of 96.8 % and 96.5 %, respectively. The CNN analyzed the validation dataset in 53 seconds, at a rate of approximately 70 frames per second. CONCLUSIONS: we developed an accurate CNN for the automatic detection of enteric protruding lesions with a wide range of morphologies. The development of these tools may enhance the diagnostic efficiency of CE.

Hepatic amyloidosis: a prevalence study and clinical characterization of a rare and severe disease.

BACKGROUND AND AIM: Amyloidosis is a systemic disease characterized by extracellular deposition of amyloid protein, most commonly in the heart and kidney. Hepatic amyloidosis is a rare form of presentation that ranges from mild hepatomegaly and altered liver biochemical tests to acute liver failure. The aims of this study were to evaluate the prevalence of amyloidosis in patients undergoing liver biopsy and describe its main clinical characteristics and prognostic impact. METHODS: A retrospective analysis of all patients with a histological diagnosis of hepatic amyloidosis between January 2010 and December 2019 was performed. MAJOR RESULTS: A total of 7 patients were identified from a total of 1773 liver biopsy procedures (0.4%), with a female predominance (6/7) and median age of diagnosis of 62 years. The most common clinical manifestations included hepatomegaly (4/7), jaundice (2/7) and peripheral edema (2/7), whereas 3/7 patients were asymptomatic. Every patient presented abnormalities in liver biochemical tests, more commonly cholestasis (6/7), but also cytolysis (4/7) or hyperbilirubinemia (2/7). Abnormal imaging findings included hepatomegaly, steatosis or parenchymal heterogeneity. In most patients (5/7), other organs were involved, most commonly with nephrotic syndrome (3/7) and infiltrative cardiomyopathy (3/7). The most common type was AA amyloidosis (3/7) followed by AL amyloidosis (2/7). The 1-year mortality rate was 43% and the median survival was 24 months. CONCLUSIONS: We report a low prevalence (0.4%) of amyloidosis among patients undergoing liver biopsy. Although rare, hepatic amyloidosis is associated with a dismal prognosis and a high index of suspicion is crucial to achieve an early diagnosis. .

The Promise of Artificial Intelligence in Digestive Healthcare and the Bioethics Challenges It Presents.

With modern society well entrenched in the digital area, the use of Artificial Intelligence (AI) to extract useful information from big data has become more commonplace in our daily lives than we perhaps realize. Medical specialties that rely heavily on imaging techniques have become a strong focus for the incorporation of AI tools to aid disease diagnosis and monitoring, yet AI-based tools that can be employed in the clinic are only now beginning to become a reality. However, the potential introduction of these applications raises a number of ethical issues that must be addressed before they can be implemented, among the most important of which are issues related to privacy, data protection, data bias, explainability and responsibility. In this short review, we aim to highlight some of the most important bioethical issues that will have to be addressed if AI solutions are to be successfully incorporated into healthcare protocols, and ideally, before they are put in place. In particular, we contemplate the use of these aids in the field of gastroenterology, focusing particularly on capsule endoscopy and highlighting efforts aimed at resolving the issues associated with their use when available.

Deep-Learning and Device-Assisted Enteroscopy: Automatic Panendoscopic Detection of Ulcers and Erosions.

Background and Objectives: Device-assisted enteroscopy (DAE) has a significant role in approaching enteric lesions. Endoscopic observation of ulcers or erosions is frequent and can be associated with many nosological entities, namely Crohn's disease. Although the application of artificial intelligence (AI) is growing exponentially in various imaged-based gastroenterology procedures, there is still a lack of evidence of the AI technical feasibility and clinical applicability of DAE. This study aimed to develop and test a multi-brand convolutional neural network (CNN)-based algorithm for automatically detecting ulcers and erosions in DAE. Materials and Methods: A unicentric retrospective study was conducted for the development of a CNN, based on a total of 250 DAE exams. A total of 6772 images were used, of which 678 were considered ulcers or erosions after double-validation. Data were divided into a training and a validation set, the latter being used for the performance assessment of the model. Our primary outcome measures were sensitivity, specificity, accuracy, positive predictive value (PPV), negative predictive value (NPV), and an area under the curve precision-recall curve (AUC-PR). Results: Sensitivity, specificity, PPV, and NPV were respectively 88.5%, 99.7%, 96.4%, and 98.9%. The algorithm's accuracy was 98.7%. The AUC-PR was 1.00. The CNN processed 293.6 frames per second, enabling AI live application in a real-life clinical setting in DAE. Conclusion: To the best of our knowledge, this is the first study regarding the automatic multi-brand panendoscopic detection of ulcers and erosions throughout the digestive tract during DAE, overcoming a relevant interoperability challenge. Our results highlight that using a CNN to detect this type of lesion is associated with high overall accuracy. The development of binary CNN for automatically detecting clinically relevant endoscopic findings and assessing endoscopic inflammatory activity are relevant steps toward AI application in digestive endoscopy, particularly for panendoscopic evaluation.

Design of a Convolutional Neural Network as a Deep Learning Tool for the Automatic Classification of Small-Bowel Cleansing in Capsule Endoscopy.

Background and objectives: Capsule endoscopy (CE) is a non-invasive method to inspect the small bowel that, like other enteroscopy methods, requires adequate small-bowel cleansing to obtain conclusive results. Artificial intelligence (AI) algorithms have been seen to offer important benefits in the field of medical imaging over recent years, particularly through the adaptation of convolutional neural networks (CNNs) to achieve more efficient image analysis. Here, we aimed to develop a deep learning model that uses a CNN to automatically classify the quality of intestinal preparation in CE. Methods: A CNN was designed based on 12,950 CE images obtained at two clinical centers in Porto (Portugal). The quality of the intestinal preparation was classified for each image as: excellent, ≥90% of the image surface with visible mucosa; satisfactory, 50-90% of the mucosa visible; and unsatisfactory, <50% of the mucosa visible. The total set of images was divided in an 80:20 ratio to establish training and validation datasets, respectively. The CNN prediction was compared with the classification established by consensus of a group of three experts in CE, currently considered the gold standard to evaluate cleanliness. Subsequently, how the CNN performed in diagnostic terms was evaluated using an independent validation dataset. Results: Among the images obtained, 3633 were designated as unsatisfactory preparation, 6005 satisfactory preparation, and 3312 with excellent preparation. When differentiating the classes of small-bowel preparation, the algorithm developed here achieved an overall accuracy of 92.1%, with a sensitivity of 88.4%, a specificity of 93.6%, a positive predictive value of 88.5%, and a negative predictive value of 93.4%. The area under the curve for the detection of excellent, satisfactory, and unsatisfactory classes was 0.98, 0.95, and 0.99, respectively. Conclusions: A CNN-based tool was developed to automatically classify small-bowel preparation for CE, and it was seen to accurately classify intestinal preparation for CE. The development of such a system could enhance the reproducibility of the scales used for such purposes.

Artificial intelligence and colon capsule endoscopy: Automatic detection of ulcers and erosions using a convolutional neural network.

BACKGROUND AND AIM: Colon capsule endoscopy (CCE) has become a minimally invasive alternative for conventional colonoscopy. Nevertheless, each CCE exam produces between 50 000 and 100 000 frames, making its analysis time-consuming and prone to errors. Convolutional neural networks (CNNs) are a type of artificial intelligence (AI) architecture with high performance in image analysis. This study aims to develop a CNN model for the identification of colonic ulcers and erosions in CCE images. METHODS: A CNN model was designed using a database of CCE images. A total of 124 CCE exams performed between 2010 and 2020 in two centers were reviewed. For CNN development, a total of 37 319 images were extracted, 33 749 showing normal colonic mucosa and 3570 showing colonic ulcers and erosions. Datasets for CNN training, validation, and testing were created. The performance of the algorithm was evaluated regarding its sensitivity, specificity, positive and negative predictive values, accuracy, and area under the curve. RESULTS: The network had a sensitivity of 96.9% and a specificity of 99.9% specific for the detection of colonic ulcers and erosions. The algorithm had an overall accuracy of 99.6%. The area under the curve was 1.00. The CNN had an image processing capacity of 90 frames per second. CONCLUSIONS: The developed algorithm is the first CNN-based model to accurately detect ulcers and erosions in CCE images, also providing a good image processing performance. The development of these AI systems may contribute to improve both the diagnostic and time efficiency of CCE exams, facilitating CCE adoption to routine clinical practice.

Gastric ulcer with liver penetration.

A 54-year-old male with previous history of chronic hepatitis C complained of postprandial epigastric discomfort and weight loss. Esophagogastroduodenoscopy revealed a large gastric ulcer on the lesser antral curvature, with biopsies showing granulation tissue and inflammatory activity without other significant changes. Despite therapy with double-dose proton pump inhibitor, the ulcer had not healed or diminished in size at reevaluation endoscopy eight weeks later. Biopsies were repeated and again only revealed granulation tissue and inflammation. The patient undergone partial gastrectomy where fibrotic adhesions to liver and peritoneum were noted. Remarkably, macroscopical analysis of the surgical specimen revealed an hepatic fragment adherent to the ulcer and histopathological examination was consistent with liver penetration. Liver penetration is a rare but important complication that must be considered in the differential diagnosis of medically refractory gastric ulcers, even in the absence of hepatic tissue in endoscopic biopsies.

Bronchial-biliary fistula secondary to cholangiocarcinoma: long-term efficacy of biliary self-expandable metal stent.

A 77-year-old female with previous medical history of non-cirrhotic chronic hepatitis B and hepatocellular carcinoma treated with sequential partial hepatectomy followed by transarterial chemoembolization complained of pruritus and jaundice. Magnetic resonance cholangiopancreatography revealed a peri-hilar ill-defined stenosing lesion suggestive of malignancy. Endoscopic retrograde cholangiopancreatography with cholangioscopy confirmed a circumferential peri-hilar stenosis with fragile mucosa and tortuous dilated vessels and biopsies of this area were consistent cholangiocarcinoma. After 3 months, she presented with new-onset dyspnea and bilioptysis and abdominal computed tomography revealed a bronchial-biliary fistula. ERCP was performed to place a self-expandable metal stent in the biliary tract, which resulted in rapid clinical improvement. The patient has been followed for 2 years and remains globally stable with two episodes of worsening of bilioptysis secondary to stent obstruction by lithiasis that were easily resolved with Fogarty balloon-assisted extraction, with rapid improvement. This case demonstrates the long-term efficacy of endoscopic biliary drainage with self-expandable metallic stent for bronchial-biliary fistula in the setting of cholangiocarcinoma. .

Capsule enteroscopy versus radionuclide scintigraphy for the diagnosis of obscure gastrointestinal bleeding.

BACKGROUND: capsule enteroscopy (CE) and 99mTc red blood cell (RBC) scintigraphy are frequently used in the investigation of obscure gastrointestinal bleeding (OGIB). There are few data comparing both diagnostic modalities. This study aimed to assess the performance of CE and scintigraphy for the diagnosis of OGIB. METHODS: patients who underwent CE and scintigraphy for OGIB were selected and analyzed retrospectively. The hemorrhagic potential of CE findings was rated using Saurin's classification. The concordance between both diagnostic techniques for bleeding detection and localization was analyzed. RESULTS: eighty-five patients (62 % female), with a median age of 63 years, were included in the study. Capsule enteroscopy identified 37 patients (43 %) with high hemorrhagic potential (P2) lesions. Most scintigraphy exams were positive for gastrointestinal bleeding (82 %). No concordance was found in the detection of lesions with hemorrhagic potential between CE and scintigraphy (kappa < 0). The distribution of P0, P1 and P2 findings was similar in patients with positive or negative scintigraphy (p = 0.526). There was no agreement regarding the location of P2 findings in CE and the bleeding detected by scintigraphy (kappa < 0). Patients with P2 lesions had significantly lower median levels of hemoglobin (p = 0.002) at presentation. No significant differences were found in hemoglobin values between patients with positive and negative scintigraphy (p = 0.058). CONCLUSION: significant diagnostic discrepancy was observed between CE and scintigraphy. The findings of CE correlated better with hemoglobin values at presentation than scintigraphy results. Therefore, scintigraphy does not appear to be useful in the diagnostic workup of OGIB.

Duodenal-type follicular lymphoma: a silent tumor.

A 79-year-old male, with a past medical history of hypertension, dyslipidemia and type 2 diabetes, underwent routine esophagogastroduodenoscopy and colonoscopy. The patient was asymptomatic, with unremarkable blood tests. Upper gastrointestinal endoscopy showed multiple whitish nodular lesions in the first part of the duodenum.

Transient splenic elastography predicts high-risk esophageal varices in patients with non-cirrhotic portal hypertension.

BACKGROUND AND AIMS: Non-cirrhotic portal hypertension (NCPH) comprise a group of diseases that cause portal hypertension without cirrhosis, leading to a high risk of hemorrhage from esophageal varices. There are no non-invasive predictors of high-risk varices (HRV) described in the literature for NCPH. This study aimed to evaluate whether transient splenic elastography (TSE) can predict HRV in patients with NCPH. METHODS: Prospective study of patients with NCPH who underwent a single timepoint evaluation with transient liver and spleen elastography, ultrasonography, upper endoscopy, and laboratory tests. The study was performed from January to September 2020. Patients were divided into two groups based on the presence of HRV. The relation between TSE, transient liver elastography (TLE), spleen size, and platelet count to the presence of HRV was evaluated. RESULTS: Of 42 patients with NCPH, 50% (21/42) presented HRV. In univariate analysis, TSE (median, 58.4 vs. 28.3, p = 0.009) and spleen size (median, 17.5 vs. 14.5 cm, p = 0.013) were associated with HRV. No statistically significant relationship was found between the presence of HRV and platelet count or TLE. In multivariate analysis, TSE was the only variable related to HRV (OR 1.21, 95% CI 1.02-1.38). TSE had a good performance in predicting HRV in our population (AUROC 0.878; 95% CI 0.751-1000). TSE > 35.4 kPa presents 93.3% sensitivity, 60.0% specificity, and 90.9% negative predictive value. CONCLUSION: In our population of patients with NCPH, TSE is useful in predicting HRV. TLE, spleen size, and platelet count were not related to HRV.

Deep Learning and Device-Assisted Enteroscopy: Automatic Detection of Gastrointestinal Angioectasia.

Background and Objectives: Device-assisted enteroscopy (DAE) allows deep exploration of the small bowel and combines diagnostic and therapeutic capacities. Suspected mid-gastrointestinal bleeding is the most frequent indication for DAE, and vascular lesions, particularly angioectasia, are the most common etiology. Nevertheless, the diagnostic yield of DAE for the detection of these lesions is suboptimal. Deep learning algorithms have shown great potential for automatic detection of lesions in endoscopy. We aimed to develop an artificial intelligence (AI) model for the automatic detection of angioectasia DAE images. Materials and Methods: A convolutional neural network (CNN) was developed using DAE images. Each frame was labeled as normal/mucosa or angioectasia. The image dataset was split for the constitution of training and validation datasets. The latter was used for assessing the performance of the CNN. Results: A total of 72 DAE exams were included, and 6740 images were extracted (5345 of normal mucosa and 1395 of angioectasia). The model had a sensitivity of 88.5%, a specificity of 97.1% and an AUC of 0.988. The image processing speed was 6.4 ms/frame. Conclusions: The application of AI to DAE may have a significant impact on the management of patients with suspected mid-gastrointestinal bleeding.

Lewis score: a useful tool for diagnosis and prognosis in Crohn's disease.

BACKGROUND: videocapsule endoscopy (VCE) is currently the most sensitive diagnostic tool to detect early small bowel inflammation. A Lewis score (LS) of ≥ 135 as the cutoff value for the presence of significant inflammatory activity in patients undergoing VCE for suspected Crohn's disease (CD) has been suggested as a useful tool for the diagnosis of CD. The aim of this study was to evaluate the diagnostic and prognostic accuracy of the LS in patients with suspected CD undergoing VCE. METHODS: a retrospective single-center study was performed that included patients who underwent VCE for suspected CD between January 2010 and December 2015. Inflammatory activity was assessed with the LS. Patients were grouped according to the criteria of the International Conference on Capsule Endoscopy (ICCE) for the definition of suspected CD; group 1: patients not fulfilling ICCE and group 2: patients with ≥ 2 ICCE criteria. RESULTS: one hundred and ninety-one patients were included, 61% were female and the mean age was 39 ± 14 years. VCE detected significant inflammatory activity (LS ≥ 135) in 81 patients (42%); 24 patients from group 1 (32%) and 57 patients from group 2 (50%) (p = 0.014). During a mean follow-up period of 41 ± 21 months (12-79), a CD diagnosis was determined in 60 patients (31%); 55 patients with LS ≥ 135 (92%) and five patients with LS < 135 (5%) (p < 0.001). The LS showed a good diagnostic accuracy with an AUROC of 0.93 (p < 0.001). During the first year after diagnosis, there was a significant association between a higher LS and the need for immunomodulatory therapy, biological therapy, bowel resection surgery or hospital admission due to a CD flare-up. CONCLUSIONS: the LS (cutoff ≥ 135) is very useful in the diagnosis of CD in patients undergoing VCE. Moreover, higher values of this score was associated with prognostic variables.

Small bowel enteroscopy - A joint clinical guideline by the Spanish and Portuguese small-bowel study groups.

The present evidence-based guidelines are focused on the use of device-assisted enteroscopy in the management of small-bowel diseases. A panel of experts selected by the Spanish and Portuguese small-bowel study groups reviewed the available evidence focusing on the main indications of this technique, its role in the management algorithm of each indication, and its diagnostic and therapeutic yield. A set of recommendations was issued accordingly.

How should we select suspected Crohn's disease patients for capsule enteroscopy?

Background: In suspected Crohn's disease (CD), non-diagnostic ileocolonoscopies are often followed by small bowel capsule endoscopy (SBCE). Adequate pre-selection of patients for SBCE is a key to optimize allocation of resources. We aimed to establish a rational approach for the CD diagnostic workflow, based on biochemical profile of patients with suspected CD, targeting an optimization of patients' selection for SBCE. Methods: Multicenter cohort study includes consecutive patients with suspected undergoing SBCE after non-diagnostic ileocolonoscopy. Minimum follow-up period after the capsule enteroscopy was six months. The outcome was confirmation of CD diagnosis. Univariate analysis and logistic regression were performed. Results: In included 220 patients, 62.3% of women were with a mean age of 41 years [26-54]. A confirmed diagnosis of CD was established in 98 patients (44.5%). The initial univariate analysis identified variables above the threshold of marginal statistical association toward CD diagnosis (p < .15). The regression model identified high CRP levels (OR 1.028 p = .128) and low serum Iron (OR 0.990 p = .025) as the independent variables with consistent correlation with CD diagnosis. Those two variables present a suitable discriminative power (AUC = 0.669, p < .001) for the diagnosis of CD. Conclusion: In suspected CD, low serum iron and elevated CRP had a statistically significant association with CD diagnosis, being helpful to identify patients with higher CD probability before SBCE. However, the lack of a proper validation of the model leads us to currently recommend SBCE to all patients with suspected CD and negative ileocolonoscopy, as no specific biochemical profile can be used to confidently exclude small bowel CD.

Treatment response in primary biliary cholangitis: The role of autoimmune hepatitis features.

INTRODUCTION AND AIM: Primary biliary cholangitis is a rare disease with scarce epidemiological data in Southern Europe. The authors aimed to evaluate treatment response in a cohort of patients. MATERIALS AND METHODS: This retrospective observational single-centre study included patients with diagnostic criteria of primary biliary cholangitis. Data on disease presentation, laboratory results, treatment and clinical endpoints were collected and analyzed. RESULTS: Fifty-three patients were included, 89% women, with mean age of 62±15 years at diagnosis. The majority was asymptomatic (49%), tested positive for antimitochondrial antibodies (96%) and had increased alkaline phosphatase (median=214U/L). 75% of the patients had liver histology and the majority were in Ludwig's stage I (42%). Autoimmune hepatitis (AIH) features were found in seven patients (13%). All were treated with ursodeoxycholic acid (UDCA) and 56% achieved biochemical response at one year; patients with AIH features exhibited steeper decreases in alkaline phosphatase (p=0.007) and reached the endpoint of 40% decrease in alkaline phosphatase more frequently (p=0.017). CONCLUSION: In conclusion a significant proportion of patients failed to achieve an adequate response to UDCA treatment. The response rate of patients with AIH features was better, which could be related to a different phenotype or to the potential impact of immunosuppressive agents.