Pathologie (Heidelberg, Germany)最新文献

Digitization of histological specimens enables new computer-assisted analysis and artificial intelligence (AI)-supported diagnostics, but is hampered by a lack of standards. Interoperability between proprietary formats and clinical systems such as the Picture Archiving and Communication System (PACS) or Laboratory Information Systems (LIS) poses a particular challenge. The Digital Imaging and COmmunications in Medicine (DICOM) format, adapted from radiology, offers an open, vendor-independent solution that integrates image data, metadata, and analysis results and enables interoperable exchange. A literature review shows a growing number of publications on digital and computer-assisted pathology, with DICOM increasingly being discussed as a key format. With an open-source, modular Docker pipeline, we demonstrate the practical implementation of DICOM-compliant workflows for storing and visualizing whole slide images and AI results. This creates the basis for standardized, transparent, and trustworthy digital pathology.

Lymphomas are basically divided into Hodgkin and B‑ and T‑cell lymphomas, with diagnosis based on morphological, immunohistochemical and, increasingly, molecular methods. Classic Hodgkin lymphoma is characterised by the presence of Hodgkin-Reed-Sternberg cells, which have a typical marker profile (CD30+, CD15+, Pax5+) and are diagnostically decisive despite their low number. Molecularly, these tumours can be identified as B‑cell lymphomas, with the tumour cells often possessing non-functional immunoglobulin genes.In B‑ and T‑cell lymphomas, the tumour cells in most cases are predominant in terms of numbers; they are classified primarily according to their B‑cell or T‑cell origin. In rare cases, so-called combination lymphomas occur, which combine Hodgkin and B‑ or T‑cell lymphoma components.Grey zone lymphomas exhibit characteristics of both classic Hodgkin's lymphoma and primary mediastinal large B‑cell lymphoma. They are divided into classical Hodgkin lymphoma (cHL)-like and Primary Mediastinal B‑cell lymphoma (PMBCL)-like forms, are difficult to diagnose and sometimes respond poorly to therapy.The future of lymphoma diagnostics lies in combining traditional histological methods with digital techniques and molecular analyses. Modern imaging and single-cell analyses enable more precise determination of both tumour cells and their microenvironment.In addition, novel spatial transcriptomic techniques combine different technologies. 4D techniques allow motility analyses of living tissue sections and thus direct observations of cell interactions. So, it seems possible to characterize localized single cells in tissues using hybridization and sequencing technologies to provide further information of cell-cell interactions and the resulting molecular changes by bioinformatics.These developments open new perspectives for personalised medicine and could help to better predict the success of innovative cell therapies. This article was created to accompany the 62nd IAP Symposium, "Lymph Nodes and Lymphoma Pathology-The Essentials."

Background: Pathological routine diagnostics generate extensive datasets, yet their potential for spatial epidemiological analyses-for instance for studying disease distributions, environmental exposures, or healthcare structures-has so far remained largely untapped.

Objective: With REDPath (Spatial Epidemiological Data Analysis of Pathology Data), a web-based tool to unlock this data source has been developed. Its goal is to visualize oncological disease burden and healthcare provision across different geographic levels, thereby supporting data-driven prevention and resource allocation strategies.

Materials and methods: The basis consists of 41,707 oncological diagnoses (ICD-10: C00-C97, 2019-2025) from the Institute of Pathology, University Medical Center Mainz, supplemented by demographic and environmental context variables. REDPath was programmed in C++ and Python; visualizations were generated with Leaflet and statistical analyses were performed in R using the lme4 and CARBayes packages. Data were processed on two levels (individual/aggregated) to ensure data protection and differentiated access rights.

Results: REDPath comprises three modules: (1) descriptive analyses for interactive visualization of disease distributions; (2) statistical models to examine spatial relationships and autocorrelations; and (3) a health services module currently in development, visualizing submitting institutions.

Conclusion: REDPath enables spatial epidemiological analysis of routine pathology data-in a user-friendly and accessible manner without statistical expertise. Its modular structure allows for the integration of additional disease entities and data sources, positioning the tool as an interface between pathology and epidemiology, with direct relevance for evidence-based healthcare planning.

Diffuse large B‑cell lymphomas are highlighted within the group of high-grade B-cell lymphomas, as they represent one of the most common lymphoma types in our region. In addition, there are numerous rare entities that can often be suspected morphologically and clinically but need to be confirmed molecularly or through extended immunophenotyping panels. Hodgkin lymphomas are also common, characterized by a few typical tumor cells and a pronounced variability in their microenvironment, leading to classification into characteristic subtypes.

Heterotopia refers to the presence of normal cells or tissue located in anatomically unusual sites of the body. In the gastrointestinal tract, most heterotopic lesions do not cause any symptoms, but rarely, they can be mistaken for neoplasms during endoscopic examinations. A 45-year-old man presented with lower abdominal pain and blood in the stool. Macroscopically, a polypoid lesion was found and resected by endoscopic submucosal dissection. Histologically, ectopic gastric mucosa, salivary gland-like structures compatible with bronchial glands, and respiratory epithelium were found. This combination of a gastric and respiratory ectopia has so far been rarely documented in the literature.

This article provides a practice-oriented overview of how thoracic diseases can be reliably assessed using small specimens. It demonstrates how cytologic and histologic methods complement each other, why careful preanalytical handling and specimen triage/tissue stewardship are critical, and how ancillary studies can be used judiciously to conserve tissue. The article also addresses current developments such as digital analysis workflows and offers practical recommendations-including common pitfalls and guidance on interpreting results.

The increasing complexity and individualization of oncologic diagnostics and therapy present new challenges for pathology. At the same time, artificial intelligence (AI) is evolving from a futuristic concept into a core area of digital medicine. With the availability of digital whole-slide images (WSIs) and increasingly powerful deep learning architectures, the number of publications in digital pathology has risen almost exponentially since around 2019.At the algorithmic level, numerous innovations have emerged in recent years: convolutional neural networks (CNNs), which initially dominated the field, are increasingly being replaced by Vision Transformer (ViT)-based models. Since 2023, foundation models have gained rapid importance due to their broad applicability and generalizability.Proof-of-concept studies have repeatedly demonstrated that AI-based solutions can improve the efficiency and sensitivity of diagnostic workflows. Several AI algorithms for histopathology have already been approved by U.S. and European regulatory agencies. More recent developments, such as vision-language models (VLMs), enable the multimodal integration of text and image data, opening up new interactive possibilities in diagnostics.Overall, the field is at the transition from proof-of-concept studies toward clinical implementation. In particular, foundation models have the potential to fundamentally reshape the structure of histopathological diagnostics in the near future. However, technical, legal, and socio-psychological barriers must still be overcome before widespread clinical adoption can be achieved.