Quantitative Morphometry and Machine Learning Model to Explore Duodenal and Rectal Mucosal Tissue of Children with Environmental Enteric Dysfunction

Review article: Faecal biomarkers for assessing small intestinal damage in coeliac disease and environmental enteropathy

BACKGROUND

In coeliac disease and environmental enteropathy, dietary gluten and enteric infections cause reversible inflammation and morphological changes to the small intestinal mucosa that can be detected in biopsy samples obtained by endoscopy. However, there is a clear need for non-invasive biomarkers. Constant shedding of mucosal material into the bowel lumen and faeces, together with easy availability of stool, makes it an interesting sample matrix.

AIMS

To conduct a systematic literature search and summarize the existing evidence for host mucosa-derived faecal biomarkers in evaluating small intestinal damage.

METHODS

We searched for studies on PubMed (MEDLINE) until 1 March 2024.

RESULTS

We identified 494 studies and included 35 original case-control and cohort studies. These assessed host mucosal transcripts and 14 other markers aiming specifically to reflect inflammation and cell-mediated, innate and gluten-induced immune responses. In coeliac disease, faecal calprotectin and anti-gliadin, tissue transglutaminase, endomysium and deamidated gliadin peptide antibodies were the most studied but with inconsistent results. Single studies reported positive findings about microRNA transcripts, β-defensin-2, lipocalin-2, zonulin-related proteins and angiotensin-converting enzyme. In environmental enteropathy, a non-significant association was reported between calprotectin and urine lactulose/mannitol ratio; there were conflicting results for neopterin, myeloperoxidase and host transcripts. Single studies reported a positive association for lactoferrin, and a negative association for regenerating islet-derived protein 1. Studies comparing faecal markers against small intestinal biopsy findings were not identified in environmental enteropathy.

CONCLUSIONS

Further studies are needed to determine reliable faecal markers as a proxy for small intestinal mucosal damage.

Advancements in Computer-Aided Diagnosis of Celiac Disease: A Systematic Review

Celiac disease, a chronic autoimmune condition, manifests in those genetically prone to it through damage to the small intestine upon gluten consumption. This condition is estimated to affect approximately one in every hundred individuals worldwide, though it often goes undiagnosed. The early and accurate diagnosis of celiac disease (CD) is critical to preventing severe health complications, with computer-aided diagnostic approaches showing significant promise. However, there is a shortage of review literature that encapsulates the field's current state and offers a perspective on future advancements. Therefore, this review critically assesses the literature on the role of imaging techniques, biomarker analysis, and computer models in improving CD diagnosis. We highlight the diagnostic strengths of advanced imaging and the non-invasive appeal of biomarker analyses, while also addressing ongoing challenges in standardization and integration into clinical practice. Our analysis stresses the importance of computer-aided diagnostics in fast-tracking the diagnosis of CD, highlighting the necessity for ongoing research to refine these approaches for effective implementation in clinical settings. Future research in the field will focus on standardizing CAD protocols for broader clinical use and exploring the integration of genetic and protein data to enhance early detection and personalize treatment strategies. These advancements promise significant improvements in patient outcomes and broader implications for managing autoimmune diseases.

Pathologist-trained machine learning classifiers developed to quantitate celiac disease features differentiate endoscopic biopsies according to modified marsh score and dietary intervention response

BACKGROUND

Histologic evaluation of the mucosal changes associated with celiac disease is important for establishing an accurate diagnosis and monitoring the impact of investigational therapies. While the Marsh-Oberhuber classification has been used to categorize the histologic findings into discrete stages (i.e., Type 0-3c), significant variability has been documented between observers using this ordinal scoring system. Therefore, we evaluated whether pathologist-trained machine learning classifiers can be developed to objectively quantitate the pathological changes of villus blunting, intraepithelial lymphocytosis, and crypt hyperplasia in small intestine endoscopic biopsies.

METHODS

A convolutional neural network (CNN) was trained and combined with a secondary algorithm to quantitate intraepithelial lymphocytes (IEL) with 5 classes on CD3 immunohistochemistry whole slide images (WSI) and used to correlate feature outputs with ground truth modified Marsh scores in a total of 116 small intestine biopsies.

RESULTS

Across all samples, median %CD3 counts (positive cells/enterocytes) from villous epithelium (VE) increased with higher Marsh scores (Type 0%CD3 VE = 13.4; Type 1-3%CD3 VE = 41.9, p < 0.0001). Indicators of villus blunting and crypt hyperplasia were also observed (Type 0-2 villous epithelium/lamina propria area ratio = 0.81; Type 3a-3c villous epithelium/lamina propria area ratio = 0.29, p < 0.0001), and Type 0-1 crypt/villous epithelial area ratio = 0.59; Type 2-3 crypt/villous epithelial area ratio = 1.64, p < 0.0001). Using these individual features, a combined feature machine learning score (MLS) was created to evaluate a set of 28 matched pre- and post-intervention biopsies captured before and after dietary gluten restriction. The disposition of the continuous MLS paired biopsy result aligned with the Marsh score in 96.4% (27/28) of the cohort.

CONCLUSIONS

Machine learning classifiers can be developed to objectively quantify histologic features and capture additional data not achievable with manual scoring. Such approaches should be further investigated to improve biopsy evaluation, especially for clinical trials.