Endometrial Pipelle Biopsy Computer-Aided Diagnosis: A Feasibility Study.

Endometrial biopsies are important in the diagnostic workup of women who present with abnormal uterine bleeding or hereditary risk of endometrial cancer. In general, approximately 10% of all endometrial biopsies demonstrate endometrial (pre)malignancy that requires specific treatment. As the diagnostic evaluation of mostly benign cases results in a substantial workload for pathologists, artificial intelligence (AI)-assisted preselection of biopsies could optimize the workflow. This study aimed to assess the feasibility of AI-assisted diagnosis for endometrial biopsies (endometrial Pipelle biopsy computer-aided diagnosis), trained on daily-practice whole-slide images instead of highly selected images. Endometrial biopsies were classified into 6 clinically relevant categories defined as follows: nonrepresentative, normal, nonneoplastic, hyperplasia without atypia, hyperplasia with atypia, and malignant. The agreement among 15 pathologists, within these classifications, was evaluated in 91 endometrial biopsies. Next, an algorithm (trained on a total of 2819 endometrial biopsies) rated the same 91 cases, and we compared its performance using the pathologist's classification as the reference standard. The interrater reliability among pathologists was moderate with a mean Cohen's kappa of 0.51, whereas for a binary classification into benign vs (pre)malignant, the agreement was substantial with a mean Cohen's kappa of 0.66. The AI algorithm performed slightly worse for the 6 categories with a moderate Cohen's kappa of 0.43 but was comparable for the binary classification with a substantial Cohen's kappa of 0.65. AI-assisted diagnosis of endometrial biopsies was demonstrated to be feasible in discriminating between benign and (pre)malignant endometrial tissues, even when trained on unselected cases. Endometrial premalignancies remain challenging for both pathologists and AI algorithms. Future steps to improve reliability of the diagnosis are needed to achieve a more refined AI-assisted diagnostic solution for endometrial biopsies that covers both premalignant and malignant diagnoses.

Machine Learning to Identify Critical Biomarker Profiles in New SARS-CoV-2 Variants.

The global dissemination of SARS-CoV-2 resulted in the emergence of several variants, including Alpha, Alpha + E484K, Beta, and Omicron. Our research integrated the study of eukaryotic translation factors and fundamental components in general protein synthesis with the analysis of SARS-CoV-2 variants and vaccination status. Utilizing statistical methods, we successfully differentiated between variants in infected individuals and, to a lesser extent, between vaccinated and non-vaccinated infected individuals, relying on the expression profiles of translation factors. Additionally, our investigation identified common causal relationships among the translation factors, shedding light on the interplay between SARS-CoV-2 variants and the host's translation machinery.

[Digital pathology in Austria]. / Digitale Pathologie in Österreich.

The situation regarding digital pathology in Austria is manageable compared to other countries. Active Austrian examples are the consortium EMPAIA, the private-public partnership Bigpicture, the Austrian Society for Clinical Pathology and Molecular Pathology (OEGPath), the company TissueGnostics, and the Austrian Platform for Personalized Medicine (OEPPM).

Superficial leiomyomas of the gastrointestinal tract with interstitial cells of Cajal.

OBJECTIVE: Some authors suggest common origin of gastrointestinal stromal tumors from stem cells, which may show diverse differentiation. There are reports in which cells morphologically identical to the interstitial cells of Cajal are found in deep leiomyomas. The aim of this study was to demonstrate CD117 positive cells in superficial gastrointestinal (GI) leiomyomas and to find other cells that would suggest diverse differentiation in histologically typical leiomyoma. MATERIALS AND METHODS: We analyzed 8 cases of superficial leiomyomas and one deep leiomyoma, received in our institutions as endoscopically or surgically obtained material. The tumor sections were immunohistochemicaly stained with CD117, CD34, NF, S100, αSMA, desmin, caldesmon and mast cell antigen. RESULTS: All leiomyomas showed diffuse positivity for αSMA, caldesmon and desmin. All of them had CD117 and CD34 positive cells morphologically identical to the interstitial cells of Cajal between smooth muscle fibers, 5 had S-100 and NF positive cells and 2 showed positivity for GFAP. The cells were found in different quantity; they were usually diffusely scattered through the tumors without predilection site, forming small groups in some areas. CONCLUSION: CD177, CD34, S-100 and NF positive cells are present in superficial leiomyomas and they may suggest common origin of GI stromal tumors.