Hartman, Douglas J.

Abstract Background and Aims To correlate histologic activity in surveillance colonoscopies with the development of colorectal neoplasia in ulcerative colitis [UC]. Methods Colorectal biopsies during surveillance [N = 764] from 52 UC patients with colorectal neoplasia were compared to 122 patients without neoplasia enrolled in a prospective natural history registry. All biopsies were scored using validated histologic scoring systems (Geboes score, Nancy histopathologic index [NHI], and Robarts histopathologic index [RHI]). Clinical, endoscopic, and histologic data were correlated with the development of colorectal neoplasia. Results In multivariable analysis, mean RHI (hazard ratio [HR] 1.07 for each 1-unit increase in RHI, 95% confidence interval [CI] 1.03–1.12, p = 0.002) and mean NHI [HR 1.89 for each 1-unit increase in NHI, 95% CI 1.34–2.67, p = 0.002] for the entire surveillance period were significantly associated with colorectal neoplasia development. Shorter surveillance interval and increasing age were associated with increased risk of neoplasia development whereas mean Mayo endoscopic score was not significant. To generate a clinically useful measure of neoplasia risk, mean histologic activity in the preceding 5 years before the study endpoint was correlated with neoplasia development. In the preceding 5 years of surveillance, a mean RHI ≥ 8 had a 7.53-fold increased risk [95% CI 2.56–12.16, p < 0.001] and mean NHI ≥ 1.9 had a 5.89-fold increased risk [95% CI 2.18–15.92, p < 0.001] of developing colorectal neoplasia. Conclusions Persistent histologic activity during multiple surveillance episodes is an independent predictor of colorectal neoplasia. Mean RHI and mean NHI during a 5-year colonoscopic surveillance period can be used to assess risk for colorectal neoplasia and optimize UC surveillance.

AimsTo develop and validate a deep learning algorithm to quantify a broad spectrum of histological features in colorectal carcinoma.Methods and resultsA deep learning algorithm was trained on haematoxylin and eosin‐stained slides from tissue microarrays of colorectal carcinomas (N = 230) to segment colorectal carcinoma digitised images into 13 regions and one object. The segmentation algorithm demonstrated moderate to almost perfect agreement with interpretations by gastrointestinal pathologists, and was applied to an independent test cohort of digitised whole slides of colorectal carcinoma (N = 136). The algorithm correctly classified mucinous and high‐grade tumours, and identified significant differences between mismatch repair‐proficient and mismatch repair‐deficient (MMRD) tumours with regard to mucin, inflammatory stroma, and tumour‐infiltrating lymphocytes (TILs). A cutoff of >44.4 TILs per mm2 carcinoma gave a sensitivity of 88% and a specificity of 73% in classifying MMRD carcinomas. Algorithm measures of tumour budding (TB) and poorly differentiated clusters (PDCs) outperformed TB grade derived from routine sign‐out, and compared favourably with manual counts of TB/PDCs with regard to lymphatic, venous and perineural invasion. Comparable associations were seen between algorithm measures of TB/PDCs and manual counts of TB/PDCs for lymph node metastasis (all P < 0.001); however, stronger correlations were seen between the proportion of positive lymph nodes and algorithm measures of TB/PDCs. Stronger associations were also seen between distant metastasis and algorithm measures of TB/PDCs (P = 0.004) than between distant metastasis and TB (P = 0.04) and TB/PDC counts (P = 0.06).ConclusionsOur results highlight the potential of deep learning to identify and quantify a broad spectrum of histological features in colorectal carcinoma.

Abstract Background Immunoglobulin G subclass 4 (IgG4) is hypothesized to play an immunomodulatory role, downregulating humoral immune responses. The role of this anti-inflammatory molecule in inflammatory bowel disease (IBD) has not been fully characterized. We sought to define alterations in serum IgG4 in patients with IBD and their association with multiyear disease severity. Methods We analyzed metadata derived from curated electronic health records from consented patients with IBD prospectively followed at a tertiary center over a 10-year time period. Patients with IBD with IgG4 serum levels available formed the study population. Demographics and multiyear clinical data were collected and analyzed. We stratified patients with IBD with low, normal, or high serum IgG4 levels. Results We found IgG4 characterized in 1193 patients with IBD and low IgG4 levels in 233 patients (20%) and elevated IgG4 levels in 61 patients (5%). An IgG4 deficiency did not significantly correlate with other antibody deficiencies. In a multiple Poisson regression analysis, low IgG4 was associated with more years on biologic agents (P = 0.002) and steroids (P = 0.049) and more hospital admissions (P &lt; 0.001), clinic visits (P = 0.010), outpatient antibiotic prescriptions (P &lt; 0.001), and CD-related surgeries (P = 0.011) during the study period after controlling for certain confounders. Elevated IgG4 was only associated with primary sclerosing cholangitis (P = 0.011). A cohort of patients with IgG4-deficient severe IBD received intravenous Ig replacement therapy, which benefited and was continued in 10 out of 11 individuals. Conclusions An IgG4 subclass deficiency, distinct from other antibody deficiencies, occurred commonly in a referral IBD population and was associated with multiple markers of disease severity. This is the first association of IgG4 subclass deficiency with an inflammatory disease process. Further work is needed to define the mechanistic role of IgG4 deficiency in this severe IBD subgroup.

Abstract Objectives: The accurate diagnosis of malignancy from small bile duct biopsy specimens is often challenging. This proof-of-concept study assessed the feasibility of a novel optical technology, spatial-domain low-coherence quantitative phase microscopy (SL-QPM), that assesses nanoscale structural alterations in epithelial nuclei for improving the diagnosis of malignancy in bile duct biopsy specimens. Methods: The SL-QPM analysis was performed on standard histology specimens of bile duct biopsy specimens from 45 patients. We analyzed normal cells with benign follow-up, histologically normal cells with pancreaticobiliary malignancy, and malignant epithelial cells. Results: The SL-QPM–derived nuclear nanomorphology marker can not only distinguish benign and malignant epithelial cells but can also detect features of malignancy in those cells normal by light microscopy with a discriminatory accuracy of 0.90. When combining pathology with SL-QPM, the sensitivity is improved to 88.5% from 65.4% of conventional pathology, while maintaining 100% specificity. Conclusions: SL-QPM–derived nuclear nanomorphology markers represent a novel approach for detecting malignancy from histologically normal-appearing epithelial cells, with potential as an adjunctive test in patients with negative or inconclusive pathologic diagnosis on bile duct biopsy specimens.