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Copyright of Pflüger's Archiv Für Die Gesamte Physiologie Des Menschen Und Der Tiere is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Der Pathologe is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The 5th edition of the World Health Organization (WHO) classification of tumors of the urinary tract and male genital organs introduced both general and specific changes in structure, classification, and nomenclature. This also applies to rarer tumors and tumor subtypes of the urinary system. Knowledge of these changes is relevant for routine histopathological work. This article provides an overview of the main new features of the rarer tumors and tumor subtypes of the urinary system in the new edition of the WHO classification., (© 2024. The Author(s), under exclusive licence to Springer Medizin Verlag GmbH, ein Teil von Springer Nature.)

Competing Interests: Einhaltung ethischer Richtlinien. Interessenkonflikt: H. Reis und F. Bremmer geben an, dass kein Interessenkonflikt besteht. Für diesen Beitrag wurden von den Autor/-innen keine Studien an Menschen oder Tieren durchgeführt. Für die aufgeführten Studien gelten die jeweils dort angegebenen ethischen Richtlinien. The supplement containing this article is not sponsored by industry

Background: Metabolic dysfunction-associated steatotic liver disease (MASLD), or non-alcoholic fatty liver disease (NAFLD), is a common disease that is diagnosed through manual evaluation of liver biopsies, an assessment that is subject to high interobserver variability (IBV). IBV can be reduced using automated methods., Objectives: Many existing computer-based methods do not accurately reflect what pathologists evaluate in practice. The goal is to demonstrate how these differences impact the prediction of hepatic steatosis. Additionally, IBV complicates algorithm validation., Materials and Methods: Forty tissue sections were analyzed to detect steatosis, nuclei, and fibrosis. Data generated from automated image processing were used to predict steatosis grades. To investigate IBV, 18 liver biopsies were evaluated by multiple observers., Results: Area-based approaches yielded more strongly correlated results than nucleus-based methods (⌀ Spearman rho [ρ] = 0.92 vs. 0.79). The inclusion of information regarding tissue composition reduced the average absolute error for both area- and nucleus-based predictions by 0.5% and 2.2%, respectively. Our final area-based algorithm, incorporating tissue structure information, achieved a high accuracy (80%) and strong correlation (⌀ Spearman ρ = 0.94) with manual evaluation., Conclusion: The automatic and deterministic evaluation of steatosis can be improved by integrating information about tissue composition and can serve to reduce the influence of IBV., (© 2024. The Author(s).)

Objective: Artificial intelligence (AI) holds the potential to make significant advancements in pathology. However, its actual implementation and certification for practical use are currently limited, often due to challenges related to model transferability. In this context, we investigate the factors influencing transferability and present methods aimed at enhancing the utilization of AI algorithms in pathology., Materials and Methods: Various convolutional neural networks (CNNs) and vision transformers (ViTs) were trained using datasets from two institutions, along with the publicly available TCGA-MIBC dataset. These networks conducted predictions in urothelial tissue and intrahepatic cholangiocarcinoma (iCCA). The objective was to illustrate the impact of stain normalization, the influence of various artifacts during both training and testing, as well as the effects of the NoisyEnsemble method., Results: We were able to demonstrate that stain normalization of slides from different institutions has a significant positive effect on the inter-institutional transferability of CNNs and ViTs (respectively +13% and +10%). In addition, ViTs usually achieve a higher accuracy in the external test (here +1.5%). Similarly, we showcased how artifacts in test data can negatively affect CNN predictions and how incorporating these artifacts during training leads to improvements. Lastly, NoisyEnsembles of CNNs (better than ViTs) were shown to enhance transferability across different tissues and research questions (+7% Bladder, +15% iCCA)., Discussion: It is crucial to be aware of the transferability challenge: achieving good performance during development does not necessarily translate to good performance in real-world applications. The inclusion of existing methods to enhance transferability, such as stain normalization and NoisyEnsemble, and their ongoing refinement, is of importance., (© 2024. The Author(s).)

Background: Of all urothelial carcinomas (UCs), 25% are muscle invasive and associated with a 5-year overall survival rate of 50%. Findings regarding the molecular classification of muscle-invasive urothelial carcinomas (MIUCs) have not yet found their way into clinical practice., Objectives: Prediction of molecular consensus subtypes in MIUCs with artificial intelligence (AI) based on histologic hematoxylin-eosin (HE) sections., Methods: Pathologic review and annotation of The Cancer Genome Atlas (TCGA) Bladder Cancer (BLCA) Cohort (N = 412) and the Dr. Senckenberg Institute of Pathology (SIP) BLCA Cohort (N = 181). An AI model for the prediction of molecular subtypes based on annotated histomorphology was trained., Results: For a five-fold cross-validation with TCGA cases (N = 274), an internal TCGA test set (N = 18) and an external SIP test set (N = 27), we reached mean area under the receiver operating characteristic curve (AUROC) scores of 0.73, 0.8 and 0.75 for the classification of the used molecular subtypes "luminal", "basal/squamous" and "stroma-rich". By training on correlations to individual molecular subtypes, rather than training on one subtype assignment per case, the AI prediction of subtypes could be significantly improved., Discussion: Follow-up studies with RNA extraction from various areas of AI-predicted molecular heterogeneity may improve molecular classifications and thereby AI algorithms trained on these classifications., (© 2024. The Author(s).)

Ovarian cancer is a common malignant genital neoplasm in women. Due to its frequent diagnosis only in advanced stages, prognosis is poor, although biomarker-driven targeted therapeutic approaches are evolving. These are currently based on molecular pathological analyses of homologous recombination deficiency (HRD) and the BRCA1/2 mutational status. The current CME article focuses on the histo- and molecular pathology of ovarian cancer, relevant molecular mechanisms, and test systems. These are discussed in the context of biomarker-driven targeted therapy., (© 2023. The Author(s), under exclusive licence to Springer Medizin Verlag GmbH, ein Teil von Springer Nature.)