The 1000 Mitoses Project: A Consensus-Based International Collaborative Study on Mitotic Figures Classification.

Introduction. The identification of mitotic figures is essential for the diagnosis, grading, and classification of various different tumors. Despite its importance, there is a paucity of literature reporting the consistency in interpreting mitotic figures among pathologists. This study leverages publicly accessible datasets and social media to recruit an international group of pathologists to score an image database of more than 1000 mitotic figures collectively. Materials and Methods. Pathologists were instructed to randomly select a digital slide from The Cancer Genome Atlas (TCGA) datasets and annotate 10-20 mitotic figures within a 2 mm2 area. The first 1010 submitted mitotic figures were used to create an image dataset, with each figure transformed into an individual tile at 40x magnification. The dataset was redistributed to all pathologists to review and determine whether each tile constituted a mitotic figure. Results. Overall pathologists had a median agreement rate of 80.2% (range 42.0%-95.7%). Individual mitotic figure tiles had a median agreement rate of 87.1% and a fair inter-rater agreement across all tiles (kappa = 0.284). Mitotic figures in prometaphase had lower percentage agreement rates compared to other phases of mitosis. Conclusion. This dataset stands as the largest international consensus study for mitotic figures to date and can be utilized as a training set for future studies. The agreement range reflects a spectrum of criteria that pathologists use to decide what constitutes a mitotic figure, which may have potential implications in tumor diagnostics and clinical management.

SMARCA4-Deficient Undifferentiated Tumor of the Esophagus: Diagnostic Pitfalls in Immunohistochemical Profiles.

SMARCA4-deficient undifferentiated tumors (SMARCA4-UT) are a newly described entity and are typically seen in the thoracic cavity. However, these tumors have been described in other body sites, including the esophagus. These tumors are rare, aggressive neoplasms, characterized by the loss of protein product of SMARCA4 (Brahma-related gene-1) and the preservation of INI1 (SMARCB1) expression. Here, we present two tumors of SMARCA4-UT of the esophagus with its microscopic appearance and immunohistochemical profile. We also include a literature review of SMARCA4-deficient tumors of the tubular gastrointestinal tract with their immunohistochemical and mismatch repair profiles for each specimen. Due to its non-specific histologic appearance and variable staining in expanded immunohistochemical panels, this tumor frequently overlaps with other tumor types, making the diagnosis of SMARCA4-UT challenging. These tumors are often associated with intestinal metaplasia of the esophagus and are thought to represent a high-grade undifferentiated transformation of a conventional esophageal adenocarcinoma. These tumors are typically associated with poor clinical outcomes and have poor response to conventional therapies. Currently, there are no standard guidelines for treatment of these tumors; however, palliative radiotherapy and systemic chemotherapy may provide benefit. More recently, immunotherapy and novel therapeutic targets have shown some promise for these patients.

Assessment of Pathology Domain-Specific Knowledge of ChatGPT and Comparison to Human Performance.

CONTEXT.­: Artificial intelligence algorithms hold the potential to fundamentally change many aspects of society. Application of these tools, including the publicly available ChatGPT, has demonstrated impressive domain-specific knowledge in many areas, including medicine. OBJECTIVES.­: To understand the level of pathology domain-specific knowledge for ChatGPT using different underlying large language models, GPT-3.5 and the updated GPT-4. DESIGN.­: An international group of pathologists (n = 15) was recruited to generate pathology-specific questions at a similar level to those that could be seen on licensing (board) examinations. The questions (n = 15) were answered by GPT-3.5, GPT-4, and a staff pathologist who recently passed their Canadian pathology licensing exams. Participants were instructed to score answers on a 5-point scale and to predict which answer was written by ChatGPT. RESULTS.­: GPT-3.5 performed at a similar level to the staff pathologist, while GPT-4 outperformed both. The overall score for both GPT-3.5 and GPT-4 was within the range of meeting expectations for a trainee writing licensing examinations. In all but one question, the reviewers were able to correctly identify the answers generated by GPT-3.5. CONCLUSIONS.­: By demonstrating the ability of ChatGPT to answer pathology-specific questions at a level similar to (GPT-3.5) or exceeding (GPT-4) a trained pathologist, this study highlights the potential of large language models to be transformative in this space. In the future, more advanced iterations of these algorithms with increased domain-specific knowledge may have the potential to assist pathologists and enhance pathology resident training.

Osseointegration Foundation Charity Overdenture Program Study: Part 2.

This assessment evaluated the clinical feasibility of using narrow-diameter implants to support a mandibular overdenture. Twelve patients presenting with an edentulous mandible were recruited from nine dental offices. Each patient received two to four implants in the mandible. The implants were placed without additional bone augmentation, and a total of 36 implants were placed. All sites achieved satisfactory crestal bone stability and soft tissue maintenance 1 year after the final prosthetic restoration. This investigation was supported by the Osseointegration Foundation, working in conjunction with the Zest Anchors implant company. There are three basic valuable activities that emerge from a professional foundation, which were reflected in this case study. They include disseminating information to practitioners, persuading young clinicians to become research investigators, and reaching out to patients in need of treatment who cannot afford it without a charitable opportunity.

Digital Quantification of Tumor Cellularity as a Novel Prognostic Feature of Non-Small Cell Lung Carcinoma.

Non-small cell lung carcinoma is currently staged based on the size and involvement of other structures. Tumor size may be a surrogate measure of the total number of tumor cells. A recently revised reporting system for adenocarcinoma incorporates high-risk histologic patterns, which may have increased cellular density. Modern digital image analysis tools can be utilized to automate the quantification of cells. In this study, we tested the hypothesis that tumor cellularity can be used as a novel prognostic tool for lung cancer. Digital slides from The Cancer Genome Atlas lung adenocarcinoma (ADC) data set (n = 213) and lung squamous cell carcinoma (SCC) data set (n = 90) were obtained and analyzed using QuPath. The number of tumor cells was normalized with the surface area of the tumor to provide a measure of tumor cell density. Tumor cellularity was calculated by multiplying the size of the tumor with the cell density. Major histologic patterns and grade were compared with the tumor density of the lung ADC and lung SCC cases. The overall and progression-free survival were compared between groups of high and low tumor cellularity. High-grade histologic patterns in the ADC and SCC cases were associated with greater tumor densities compared with low-grade patterns. Cases with lower tumor cellularity had improved overall and progression-free survival compared with cases with higher cellularity. These results support tumor cellularity as a novel prognostic tool for non-small cell lung carcinoma that considers tumor stage and grade elements.

Tumor-Infiltrating T Cells in EBV-Associated Gastric Carcinomas Exhibit High Levels of Multiple Markers of Activation, Effector Gene Expression, and Exhaustion.

Epstein-Barr virus (EBV) is a gamma-herpesvirus associated with 10% of all gastric cancers (GCs) and 1.5% of all human cancers. EBV-associated GCs (EBVaGCs) are pathologically and clinically distinct entities from EBV-negative GCs (EBVnGCs), with EBVaGCs exhibiting differential molecular pathology, treatment response, and patient prognosis. However, the tumor immune landscape of EBVaGC has not been well explored. In this study, a systemic and comprehensive analysis of gene expression and immune landscape features was performed for both EBVaGC and EBVnGC. EBVaGCs exhibited many aspects of a T cell-inflamed phenotype, with greater T and NK cell infiltration, increased expression of immune checkpoint markers (BTLA, CD96, CTLA4, LAG3, PD1, TIGIT, and TIM3), and multiple T cell effector molecules in comparison with EBVnGCs. EBVaGCs also displayed a higher expression of anti-tumor immunity factors (PDL1, CD155, CEACAM1, galectin-9, and IDO1). Six EBV-encoded miRNAs (miR-BARTs 8-3p, 9-5p, 10-3p, 22, 5-5p, and 14-3p) were strongly negatively correlated with the expression of immune checkpoint receptors and multiple markers of anti-tumor immunity. These profound differences in the tumor immune landscape between EBVaGCs and EBVnGCs may help explain some of the observed differences in pathological and clinical outcomes, with an EBV-positive status possibly being a potential biomarker for the application of immunotherapy in GC.

Sclerosing Pneumocytoma With Pulmonary Sarcoidosis: A Case Report.

Pulmonary sclerosing pneumocytoma, formally named pulmonary sclerosing hemangioma, is a rare benign tumor with malignant potential often identified as solid pulmonary nodules. Sarcoidosis is an inflammatory, multisystemic disease of unknown cause with a wide range of clinical manifestations. The disorder is characterized by the formation of noncaseating granulomas in virtually any organ in the body. We present a case of a patient presenting with fever, weight loss, and respiratory symptoms found to have both a sclerosing pneumocytoma and pulmonary sarcoidosis. A diagnosis was made following the lobectomy. The patient was followed for two years with stable lymphadenopathy while remaining asymptomatic.