Bulb Biopsy in Adult Celiac Disease: Pros Outweigh the Cons?

Guidelines for best practices in monitoring established coeliac disease in adult patients

Coeliac disease (CeD) is an immunological disease triggered by the consumption of gluten contained in food in individuals with a genetic predisposition. Diagnosis is based on the presence of small bowel mucosal atrophy and circulating autoantibodies (anti-type 2 transglutaminase antibodies). After diagnosis, patients follow a strict, life-long gluten-free diet. Although the criteria for diagnosis of this disease are well defined, the monitoring phase has been studied less and there is a lack of specific guidelines for this phase. To develop a set of clinical guidelines for CeD monitoring, we followed the Grading of Recommendations Assessment, Development and Evaluation methodology. Statements and recommendations with the level of evidence were developed and approved by the working group, which comprised gastroenterologists, pathologists, dieticians and biostatisticians. The proposed guidelines, endorsed by the North American and European coeliac disease scientific societies, make recommendations for best practices in monitoring patients with CeD based on the available evidence. The evidence level is low for many topics, suggesting that further research in specific aspects of CeD would be valuable. In conclusion, the present guidelines support clinicians in improving CeD treatment and follow-up and highlight novel issues that should be considered in future studies.

Computer-Based Diagnosis of Celiac Disease by Quantitative Processing of Duodenal Endoscopy Images

Celiac disease (CD) is a lifelong chronic autoimmune systemic disease that primarily affects the small bowel of genetically susceptible individuals. The diagnostics of adult CD currently rely on specific serology and the histological assessment of duodenal mucosa on samples taken by upper digestive endoscopy. Because of several pitfalls associated with duodenal biopsy sampling and histopathology, and considering the pediatric no-biopsy diagnostic criteria, a biopsy-avoiding strategy has been proposed for adult CD diagnosis also. Several endoscopic changes have been reported in the duodenum of CD patients, as markers of villous atrophy (VA), with good correlation with serology. In this setting, an opportunity lies in the automated detection of these endoscopic markers, during routine endoscopy examinations, as potential case-finding of unsuspected CD. We collected duodenal endoscopy images from 18 CD newly diagnosed CD patients and 16 non-CD controls and applied machine learning (ML) and deep learning (DL) algorithms on image patches for the detection of VA. Using histology as standard, high diagnostic accuracy was seen for all algorithms tested, with the layered convolutional neural network (CNN) having the best performance, with 99.67% sensitivity and 98.07% positive predictive value. In this pilot study, we provide an accurate algorithm for automated detection of mucosal changes associated with VA in CD patients, compared to normally appearing non-atrophic mucosa in non-CD controls, using histology as a reference.

Computer-Based Diagnosis of Celiac Disease by Quantitative Processing of Duodenal Endoscopy Images

Celiac disease (CD) is a lifelong chronic autoimmune systemic disease that primarily affects the small bowel of genetically susceptible individuals. The diagnostics of adult CD currently rely on specific serology and the histological assessment of duodenal mucosa on samples taken by upper digestive endoscopy. Because of several pitfalls associated with duodenal biopsy sampling and histopathology, and considering the pediatric no-biopsy diagnostic criteria, a biopsy-avoiding strategy has been proposed for adult CD diagnosis also. Several endoscopic changes have been reported in the duodenum of CD patients, as markers of villous atrophy (VA), with good correlation with serology. In this setting, an opportunity lies in the automated detection of these endoscopic markers, during routine endoscopy examinations, as potential case-finding of unsuspected CD. We collected duodenal endoscopy images from 18 CD newly diagnosed CD patients and 16 non-CD controls and applied machine learning (ML) and deep learning (DL) algorithms on image patches for the detection of VA. Using histology as standard, high diagnostic accuracy was seen for all algorithms tested, with the layered convolutional neural network (CNN) having the best performance, with 99.67% sensitivity and 98.07% positive predictive value. In this pilot study, we provide an accurate algorithm for automated detection of mucosal changes associated with VA in CD patients, compared to normally appearing non-atrophic mucosa in non-CD controls, using histology as a reference.

The indications for biopsy in routine upper gastrointestinal endoscopy

This review describes the indications and contraindications for endoscopic biopsy, in routine practice, of the upper gastrointestinal (GI) tract. We accept that this review provides grounds for controversy, as our stance in certain situations is counter to some national guidelines. Nevertheless, we provide evidence to support our viewpoints, especially on efficiency and economic grounds. We describe the particular controversies concerning the biopsy assessment of Barrett's oesophagus, chronic gastritis and the duodenum in the investigation of coeliac disease. We accept that there are indications for more extensive upper GI biopsy protocols in children than in adults; the latter constitute our main focus in this article. We would encourage detailed discussion between pathologists and their endoscopy colleagues about the indications, or lack of them, for routine upper GI endoscopic biopsy, as studies have shown that adherence to agreed guidelines has resulted in a very considerable diminution in the biopsy workload without compromising patient management. Furthermore, where biopsy is indicated, we emphasise the importance of accompanying clinical information provided to the pathologist, in particular regarding biopsy site(s), and regular feedback to endoscopists to improve and maintain the quality of such information. Finally, local dialogue is also advised, when necessary, to indicate to endoscopists the need to appropriately segregate biopsies into separate, individually labelled specimens, to maximise the information that can be derived by pathological evaluation and thereby improve the quality of the final pathology report.