Ten Years of Pathology on Twitter (X): Landscape and Evolution of Pathology on Twitter From 2012 to 2023.

CONTEXT.­: Social media is a powerful tool in pathology education and professional networking that connects pathologists and pathology trainees from around the world. Twitter (X) appears to be the most popular social media platform pathologists use to share pathology-related content and connect with other pathologists. Although there has been some published research on pathology-related activity on Twitter during short time frames, to date there has not been published research examining pathology-related Twitter activity in totality from its earliest days of activity to recently. OBJECTIVE.­: To comprehensively evaluate the use of pathology on Twitter (X) during the last 10 years. DESIGN.­: Pathology-related tweets were systematically scraped from Twitter from January 2012 to January 2023 using pathology hashtags as a surrogate measure for all pathology content on Twitter. COVID-related tweets were approximated by tweets containing the term "COVID." RESULTS.­: There were 591 812 unique pathology-related tweets identified during the time period, with #pathology being the most common hashtag used and #PathTwitter becoming more popular since 2020. There has been positive annual growth of pathology Twitter, with peaks in use during major pathology conferences. During the initial phases of the COVID-19 pandemic a sustained increase in pathology tweets was observed. CONCLUSIONS.­: Pathology Twitter has grown during the last 10 years and has become increasingly popular for pathology education and networking. With the changing landscape of social media platforms this study provides an understanding of how pathology medical education and professional networking uses of social media are used and evolve over time.

Performance of an Artificial Intelligence Model for Recognition and Quantitation of Histologic Features of Eosinophilic Esophagitis on Biopsy Samples.

We have developed an artificial intelligence (AI)-based digital pathology model for the evaluation of histologic features related to eosinophilic esophagitis (EoE). In this study, we evaluated the performance of our AI model in a cohort of pediatric and adult patients for histologic features included in the Eosinophilic Esophagitis Histologic Scoring System (EoEHSS). We collected a total of 203 esophageal biopsy samples from patients with mucosal eosinophilia of any degree (91 adult and 112 pediatric patients) and 10 normal controls from a prospectively maintained database. All cases were assessed by a specialized gastrointestinal (GI) pathologist for features in the EoEHSS at the time of original diagnosis and rescored by a central GI pathologist (R.K.M.). We subsequently analyzed whole-slide image digital slides using a supervised AI model operating in a cloud-based, deep learning AI platform (Aiforia Technologies) for peak eosinophil count (PEC) and several histopathologic features in the EoEHSS. The correlation and interobserver agreement between the AI model and pathologists (Pearson correlation coefficient [rs] = 0.89 and intraclass correlation coefficient [ICC] = 0.87 vs original pathologist; rs = 0.91 and ICC = 0.83 vs central pathologist) were similar to the correlation and interobserver agreement between pathologists for PEC (rs = 0.88 and ICC = 0.91) and broadly similar to those for most other histologic features in the EoEHSS. The AI model also accurately identified PEC of >15 eosinophils/high-power field by the original pathologist (area under the curve [AUC] = 0.98) and central pathologist (AUC = 0.98) and had similar AUCs for the presence of EoE-related endoscopic features to pathologists' assessment. Average eosinophils per epithelial unit area had similar performance compared to AI high-power field-based analysis. Our newly developed AI model can accurately identify, quantify, and score several of the main histopathologic features in the EoE spectrum, with agreement regarding EoEHSS scoring which was similar to that seen among GI pathologists.

Digital Hepatic Iron Content: An Artificial Intelligence Model for Spatially Resolved Histologic Iron Quantitative Analysis in Liver Samples.

Currently, the precise evaluation of tissue hepatic iron content (HIC) requires laboratory testing using tissue-destructive methods based on colorimetry or spectrophotometry. To maximize the use of routine histologic stains in this context, we developed an artificial intelligence (AI) model for the recognition and spatially resolved measurement of iron in liver samples. Our AI model was developed using a cloud-based, supervised deep learning platform (Aiforia Technologies). Using digitized Pearl Prussian blue iron stain whole slide images representing the full spectrum of changes seen in hepatic iron overload, our training set consisted of 59 cases, and our validation set consisted of 19 cases. The study group consisted of 98 liver samples from 5 different laboratories, for which tissue quantitative analysis using inductively coupled plasma mass spectrometry was available, collected between 2012 and 2022. The correlation between the AI model % iron area and HIC was Rs = 0.93 for needle core biopsy samples (n = 73) and Rs = 0.86 for all samples (n = 98). The digital hepatic iron index (HII) was highly correlated with HII > 1 (area under the curve [AUC] = 0.93) and HII > 1.9 (AUC = 0.94). The percentage area of iron within hepatocytes (vs Kupffer cells and portal tract iron) identified patients with any hereditary hemochromatosis-related mutations (either homozygous or heterozygous) (AUC = 0.65, P = .01) with at least similar accuracy than HIC, HII, and any histologic iron score. The correlation between the Deugnier and Turlin score and the AI model % iron area for all patients was Rs = 0.87 for total score, Rs = 0.82 for hepatocyte iron score, and Rs = 0.84 for Kupffer cell iron score. Iron quantitative analysis using our AI model was highly correlated with both detailed histologic scoring systems and tissue quantitative analysis using inductively coupled plasma mass spectrometry and offers advantages (related to the spatial resolution of iron analysis and the nontissue-destructive nature of the test) over standard quantitative methods.

Disproportionate increases in alcohol-associated hepatitis incidence in women and individuals of low socioeconomic status: A population-based study using the Rochester epidemiology project database.

BACKGROUND: Alcohol-associated hepatitis (AH) is among the deadliest liver diseases, but its incidence is poorly defined. The aim of our study was to define the incidence of AH meeting the National Institute on Alcohol Abuse and Alcoholism criteria and to identify risk factors for AH. METHODS: We conducted a retrospective cohort study using the Rochester epidemiology project database on adult patients hospitalized with AH between January 1, 2000 and December 31, 2018. Patients were screened using ICD-9 codes and then included if they met the National Institute on Alcohol Abuse and Alcoholism criteria on manual chart review. Baseline demographics, comorbidities, access to care, liver-related complications, and outcomes were obtained. The HOUsing-based index of SocioEconomic status index was used to measure socioeconomic status. Incidence rates were calculated in cases per 100,000 person-years of follow-up. RESULTS: Among 204 patients, the cumulative AH incidence was 6.8 per 100,000 person-years. Between 2000-2004 and 2015-2018, AH incidence among males increased from 8.4 to 14.7 per 100,000 py, whereas AH incidence among females increased by 7-fold from 0.8 to 5.9 per 100,000 py. Such increases among females were accompanied by increases in comorbid depression and anxiety. The proportion of patients with AH in the lower socioeconomic status quartiles increased from 62.9% between 2000 and 2004 to 73.3% between 2015 and 2019. CONCLUSIONS: The incidence of AH is increasing rapidly, especially among females and individuals of lower socioeconomic status. There are areas of unmet need in preventative measures and treatments for comorbid psychiatric disorders in patients at high risk of AH.

Development and technical validation of an artificial intelligence model for quantitative analysis of histopathologic features of eosinophilic esophagitis.

Background: In an attempt to provide quantitative, reproducible, and standardized analyses in cases of eosinophilic esophagitis (EoE), we have developed an artificial intelligence (AI) digital pathology model for the evaluation of histologic features in the EoE/esophageal eosinophilia spectrum. Here, we describe the development and technical validation of this novel AI tool. Methods: A total of 10 726 objects and 56.2 mm2 of semantic segmentation areas were annotated on whole-slide images, utilizing a cloud-based, deep learning artificial intelligence platform (Aiforia Technologies, Helsinki, Finland). Our training set consisted of 40 carefully selected digitized esophageal biopsy slides which contained the full spectrum of changes typically seen in the setting of esophageal eosinophilia, ranging from normal mucosa to severe abnormalities with regard to each specific features included in our model. A subset of cases was reserved as independent "test sets" in order to assess the validity of the AI model outside the training set. Five specialized experienced gastrointestinal pathologists scored each feature blindly and independently of each other and of AI model results. Results: The performance of the AI model for all cell type features was similar/non-inferior to that of our group of GI pathologists (F1-scores: 94.5-94.8 for AI vs human and 92.6-96.0 for human vs human). Segmentation area features were rated for accuracy using the following scale: 1. "perfect or nearly perfect" (95%-100%, no significant errors), 2. "very good" (80%-95%, only minor errors), 3. "good" (70%-80%, significant errors but still captures the feature well), 4. "insufficient" (less than 70%, significant errors compromising feature recognition). Rating scores for tissue (1.01), spongiosis (1.15), basal layer (1.05), surface layer (1.04), lamina propria (1.15), and collagen (1.11) were in the "very good" to "perfect or nearly perfect" range, while degranulation (2.23) was rated between "good" and "very good". Conclusion: Our newly developed AI-based tool showed an excellent performance (non-inferior to a group of experienced GI pathologists) for the recognition of various histologic features in the EoE/esophageal mucosal eosinophilia spectrum. This tool represents an important step in creating an accurate and reproducible method for semi-automated quantitative analysis to be used in the evaluation of esophageal biopsies in this clinical context.

Erdheim-Chester disease: Cytomorphologic clues for a rare histiocytic neoplasm including a distinct tigroid background pattern on smears.

We present the cytomorphologic features of Erdheim-Chester disease (ECD) from 7 patients who have a confirmed diagnosis of ECD, including correlation with the histology on the needle core biopsies. ECD is a rare multi-organ neoplastic histiocytic disorder. The most common locations of involvement are long bones, retroperitoneum, and vasculature. Cytologic preparations often show scant cellularity. Even when neoplastic histiocytes are present on smears, they may be readily overlooked as they are not typically monomorphic, and instead exhibit a variety of morphologies from epithelioid to spindled, with multinucleated giant cells variably present. To our knowledge, ours is the first description of a distinct reticular or tigroid background on smears that is variably present due to rupture of the foamy neoplastic cells. Typically, smears from a targeted mass lesion from any site showing scant polymorphous histiocytes would be regarded as non-diagnostic. A diagnosis of ECD in all cases was based on the needle core biopsy with corresponding immunohistochemical (IHC) stains and BRAF mutational analysis, except for one case in which molecular analysis was not able to be performed. We present these cases to alert practicing cytopathologists to the pitfalls related to the highly variable location, smear cellularity, and cytomorphology of ECD, which should prompt the request of dedicated tissue cores at the time of rapid on-site evaluation and trigger careful clinical-radiologic correlation, as well as consultation with hematopathology colleagues.

Mammary analogue secretory carcinoma: A challenging case arising in a young man with radiation exposure.

Mammary Analogue Secretory Carcinoma (MASC) is a recently described salivary gland tumor frequently sampled via fine-needle aspiration. The cytologic features of MASC are not entirely distinctive and can simulate acinic cell carcinoma, but the tumor harbors an ETV6 gene rearrangement resulting in an ETV6-NTRK3 fusion gene. We present a case of MASC arising in a 31 year old man with a history of multiple radio-embolization procedures.

Malignant gastrointestinal neuroectodermal tumor: Cytologic, histologic, immunohistochemical, and molecular pitfalls.

Malignant gastrointestinal neuroectodermal tumor (GNET) is a rare malignant primary gastrointestinal mesenchymal tumor which can be diagnosed via fine-needle aspiration (FNA) cytology. In the context of FNA, the diagnosis requires a cell block and the use of significant resources including immunohistochemical stains and molecular testing. The differential diagnosis of GNET includes clear cell sarcoma (CCS), gastrointestinal stromal tumor (GIST), gastric schwannoma, metastatic melanoma, malignant perivascular epithelioid cell tumor (PEComa) and granular cell tumor, among others. Here we describe a case which was initially diagnosed as malignant granular cell tumor by FNA which was later revised to GNET following the finding of an EWSR1-ATF1 fusion gene rearrangement.