Association of IgG4 response and autoimmune pancreatitis with intraductal papillary-mucinous neoplasms

Objectives: Concurrent intraductal papillary-mucinous neoplasm (IPMN) and autoimmune pancreatitis (AIP) was observed in a patient (index case) at our institution. Cases of coincidental IPMN and type 1 AIP and concurrent ductal adenocarcinoma (PDAC) and AIP have been previously reported. In this study we evaluate the hypothesis that IPMN elicits an IgG4 response. Methods: Twenty-one pancreases (including the index case) with IPMN resected at our institution were studied. H&E stained slides were reviewed and blocks of peritumoral pancreas were immunostained with IgG4 to look for IgG4-positive plasma cells. Results: We found evidence of variable IgG4 overexpression in 4/21 (19%) of IPMN. These included the index case and three others without stigmata of AIP. Conclusion: A small subset of pancreatic neoplasms including intraductal papillary-mucinous neoplasms (IPMN) is associated with an IgG4 autoimmune response that sometimes progresses to peritumoral type 1 AIP and less often to diffuse AIP and IgG4-related systemic disease

Deep-Learning for Classification of Colorectal Polyps on Whole-Slide Images

Histopathological characterization of colorectal polyps is an important principle for determining the risk of colorectal cancer and future rates of surveillance for patients. This characterization is time-intensive, requires years of specialized training, and suffers from significant inter-observer and intra-observer variability. In this work, we built an automatic image-understanding method that can accurately classify different types of colorectal polyps in whole-slide histology images to help pathologists with histopathological characterization and diagnosis of colorectal polyps. The proposed image-understanding method is based on deep-learning techniques, which rely on numerous levels of abstraction for data representation and have shown state-of-the-art results for various image analysis tasks. Our image-understanding method covers all five polyp types (hyperplastic polyp, sessile serrated polyp, traditional serrated adenoma, tubular adenoma, and tubulovillous/villous adenoma) that are included in the US multi-society task force guidelines for colorectal cancer risk assessment and surveillance, and encompasses the most common occurrences of colorectal polyps. Our evaluation on 239 independent test samples shows our proposed method can identify the types of colorectal polyps in whole-slide images with a high efficacy (accuracy: 93.0%, precision: 89.7%, recall: 88.3%, F1 score: 88.8%). The presented method in this paper can reduce the cognitive burden on pathologists and improve their accuracy and efficiency in histopathological characterization of colorectal polyps, and in subsequent risk assessment and follow-up recommendations

Automated detection of celiac disease on duodenal biopsy slides: a deep learning approach

Celiac disease prevalence and diagnosis have increased substantially in recent years. The current gold standard for celiac disease confirmation is visual examination of duodenal mucosal biopsies. An accurate computer-aided biopsy analysis system using deep learning can help pathologists diagnose celiac disease more efficiently. In this study, we trained a deep learning model to detect celiac disease on duodenal biopsy images. Our model uses a state-of-the-art residual convolutional neural network to evaluate patches of duodenal tissue and then aggregates those predictions for whole-slide classification. We tested the model on an independent set of 212 images and evaluated its classification results against reference standards established by pathologists. Our model identified celiac disease, normal tissue, and nonspecific duodenitis with accuracies of 95.3%, 91.0%, and 89.2%, respectively. The area under the receiver operating characteristic curve was greater than 0.95 for all classes. We have developed an automated biopsy analysis system that achieves high performance in detecting celiac disease on biopsy slides. Our system can highlight areas of interest and provide preliminary classification of duodenal biopsies prior to review by pathologists. This technology has great potential for improving the accuracy and efficiency of celiac disease diagnosis.Comment: Accepted in Journal of Pathology Informatic

Hyper-Methylated Loci Persisting from Sessile Serrated Polyps to Serrated Cancers

Although serrated polyps were historically considered to pose little risk, it is now understood that progression down the serrated pathway could account for as many as 15%–35% of colorectal cancers. The sessile serrated adenoma/polyp (SSA/P) is the most prevalent pre-invasive serrated lesion. Our objective was to identify the CpG loci that are persistently hyper-methylated during serrated carcinogenesis, from the early SSA/P lesion through the later cancer phases of neoplasia development. We queried the loci hyper-methylated in serrated cancers within our rightsided SSA/Ps from the New Hampshire Colonoscopy Registry, using the Illumina Infinium Human Methylation 450 k panel to comprehensively assess the DNA methylation status. We identified CpG loci and regions consistently hyper-methylated throughout the serrated carcinogenesis spectrum, in both our SSA/P specimens and in serrated cancers. Hyper-methylated CpG loci included the known the tumor suppressor gene RET (p = 5.72 x 10−10), as well as loci in differentially methylated regions for GSG1L, MIR4493, NTNG1, MCIDAS, ZNF568, and RERG. The hyper-methylated loci that we identified help characterize the biology of SSA/P development, and could be useful as therapeutic targets, or for future identification of patients who may benefit from shorter surveillance intervals

Evaluation of an Artificial Intelligence-Augmented Digital System for Histologic Classification of Colorectal Polyps

Importance: Colorectal polyps are common, and their histopathologic classification is used in the planning of follow-up surveillance. Substantial variation has been observed in pathologists\u27 classification of colorectal polyps, and improved assessment by pathologists may be associated with reduced subsequent underuse and overuse of colonoscopy. Objective: To compare standard microscopic assessment with an artificial intelligence (AI)-augmented digital system that annotates regions of interest within digitized polyp tissue and predicts polyp type using a deep learning model to assist pathologists in colorectal polyp classification. Design, Setting, and Participants: In this diagnostic study conducted at a tertiary academic medical center and a community hospital in New Hampshire, 100 slides with colorectal polyp samples were read by 15 pathologists using a microscope and an AI-augmented digital system, with a washout period of at least 12 weeks between use of each modality. The study was conducted from February 10 to July 10, 2020. Main Outcomes and Measures: Accuracy and time of evaluation were used to compare pathologists\u27 performance when a microscope was used with their performance when the AI-augmented digital system was used. Outcomes were compared using paired t tests and mixed-effects models. Results: In assessments of 100 slides with colorectal polyp specimens, use of the AI-augmented digital system significantly improved pathologists\u27 classification accuracy compared with microscopic assessment from 73.9% (95% CI, 71.7%-76.2%) to 80.8% (95% CI, 78.8%-82.8%) (P \u3c.001). The overall difference in the evaluation time per slide between the digital system (mean, 21.7 seconds; 95% CI, 20.8-22.7 seconds) and microscopic examination (mean, 13.0 seconds; 95% CI, 12.4-13.5 seconds) was -8.8 seconds (95% CI, -9.8 to -7.7 seconds), but this difference decreased as pathologists became more familiar and experienced with the digital system; the difference between the time of evaluation on the last set of 20 slides for all pathologists when using the microscope and the digital system was 4.8 seconds (95% CI, 3.0-6.5 seconds). Conclusions and Relevance: In this diagnostic study, an AI-augmented digital system significantly improved the accuracy of pathologic interpretation of colorectal polyps compared with microscopic assessment. If applied broadly to clinical practice, this tool may be associated with decreases in subsequent overuse and underuse of colonoscopy and thus with improved patient outcomes and reduced health care costs.

Adenovirus hepatitis presenting as tumoral lesions in an immunocompromised patient

A 59-year-old man with T-cell prolymphocytic leukemia on alemtuzumab presented with neutropenic fever, intermittent nausea, and multiple ill-defined low attenuation foci in the liver on abdominal computed tomography scan which were suspicious for metastatic disease. Histological examination revealed the diagnosis of adenovirus hepatitis. Patient responded well to cidofovir. Adenovirus hepatitis is a rare but important entity to be considered by the clinicians, radiologists, and pathologists. Timely diagnosis and appropriate management are essential to improve the prognosis of adenovirus hepatitis in immunocompromised patients