Ceskoslovenska patologie最新文献

Endometriosis is a chronic, estrogen-dependent, inflammatory disease characterized by the growth of endometriotic tissue outside the uterus. Among the wide spectrum of clinical manifestations of endometriosis, chronic pelvic pain, dysmenorrhea, dyspareunia and subfertility are the main symptoms that significantly reduce the quality of life of affected women. Despite the fact that endometriosis is considered a benign disease, it shares some features typical of malignant tumors. One of them is abnormal morphology, which indicates atypia of the glandular epithelium without signs of hyperplasia, or it may be glandular hyperplasia, which may or may not be accompanied by cellular atypia. This situation is reflected in the term atypical endometriosis, the diagnosis of which is not easy. Cellular atypia of a severe degree can be of reactive origin, so the mutual differentiation of dysplastic and reactive changes in endometriotic tissue is limited and problematic. Our working group from the scientific center for the treatment of endometriosis at the Znojmo Hospital recently dealt with atypical endometriosis and pointed out the potential utility of immunohistochemistry in its diagnosing. Using a simple immunohistochemical panel with antibodies against estrogen receptors, progesterone receptors, and the tumor suppressor p53, we found significantly lower levels of hormone receptor expression and increased p53 expression in atypical endometriosis compared to normal (typical) endometriosis. Due to the low number of cases analyzed and the inconsistent results of studies dealing with hormone receptors (and other markers) in atypical endometriosis, the usefulness of the immunohistochemical panel described in our study must be verified on a larger number of cases. In routine histopathological practice, atypical endometriosis is not a well-known entity. However, it is important to become familiar with it because its presence is associated with a higher incidence of a certain group of tumors known as endometriosis-associated cancers, especially endometrioid carcinoma and clear cell carcinoma.

The application of digital pathology and artificial intelligence in anatomical pathology represents a revolutionary step towards the modernization of diagnostic processes. Digitalization, primarily based on creation and subsequent use of whole slide imaging, enables generating of full digital images of histological slides, offering potential benefits in diagnostic accuracy and accessibility. Unlike traditional microscopy, digital pathology also facilitates telemedicine and remote consultation, opening new possibilities for collaboration and sharing of expertise at both national and international levels. However, implementing a digital workflow requires substantial investments in scanners, software platforms, high-capacity storage, and IT infrastructure. Despite considerable costs of implementation, it brings numerous advantages, including time savings, opportunities for centralized diagnostics, and a reduction in sample transport costs. This paper focuses on the practical aspects of implementing digital pathology in pathology laboratories, emphasizing the benefits, risks, and technological requirements associated with digitalized workflows. It also discusses crucial roles of validation and verification, which are essential for ensuring a diagnostic accuracy of digital images compared to conventional microscopy. The article presents digital pathology as a dynamically evolving field with high potential for personalized medicine, improved diagnostic accuracy, and support for remote collaboration, addressing the growing demands of modern medicine.

The study focuses on the utilization of artificial intelligence (AI) algorithms in the diagnosis of breast, lung, and prostate cancer. It describes the historical development of the digitalization of pathological processes, the implementation of artificial intelligence, and its current applications in pathology. The study emphasizes machine learning, deep learning, computer vision, and digital pathology, which contribute to the automation and refinement of diagnostics. Special attention is given to specific tools such as the uPath systems from Roche and IBEX Medical Analytics, which enable the analysis of histopathological images, tumor cell classification, and biomarker evaluation. The study also highlights the benefits of AI utilization, including increased diagnostic accuracy and efficiency in laboratory processes, while simultaneously addressing the challenges associated with its implementation, such as ethical and legal considerations, data protection, and liability for errors. The aim of this study is to provide a comprehensive overview of the potential applications of AI in digital pathology and its role in modern oncological diagnostics.

Introduction: Sudden cardiac death due to coronary atherosclerosis remains the most common cause of sudden cardiac death in the adult population. Cases of sudden death from cardiovascular causes are the most common part of autopsy forensic practice.

Material and methodology: The study included 90 cases of sudden cardiac death caused by acute changes in atherosclerotic plaques, which were retrospectively analyzed with a focus on age, sex, seasonal and circadian influences, risk factors for atherosclerosis, type and location of coronary artery occlusion and the presence of external toxicological substances.

Results: The group consisted of 63 men (70.0%) and 27 women (30.0%). The average age of the deceased was 70.1 years. Most deaths occurred in the afternoon and evening hours (39.8% in total), and the least surprisingly in the morning hours (8.9%). The most common days of death were Thursday and Friday (37.8% in total). In terms of seasons, the most deaths occurred in the winter months (34.4%). In men, the accumulation of multiple risk factors was evident - 81.0% of men had 4 or more risk factors for atherosclerosis. In women, only 40.7% of women achieved the accumulation of 4 or more risk factors. Coronary artery thrombosis was detected in 55 cases (61.1%), other forms of significant coronary artery occlusion was found in a total of 35 people (38.9%). In terms of localization of the occlusion, the anterior interventricular branch (RIVA) of the left coronary artery was most often affected in both sexes, in a total of 67 people (74.4%). The most common phenomenon was the simultaneous involvement of two coronary arteries (36.7% of cases).

Conclusion: The authors analysed the morphological findings on the coronary arteries in cases of fatal coronary atherosclerosis with special focus on acute changes in atherosclerotic plaques, anamnestic data of the deceased with an emphasis on risk factors for atherosclerosis, and circadian and seasonal influences on the incidence of fatal coronary atherosclerosis. The study confirmed an increased incidence of acute coronary events in the winter period, on working days and in the home in both sexes. An important accompanying phenomenon was the accumulation of risk factors, especially in men. Another follow-up and larger study will be suitable for an exact evaluation of the identified phenomena.

The biliary tree comprises a three-dimensional network of intrahepatic and extrahepatic ducts lined by biliary epithelium (cholangiocytes). The bile ducts and cholangiocytes may be affected by a broad spectrum of disorders collectively referred to as cholangiopathies. These conditions are classified based on anatomical aspects (predominantly affecting small or large bile ducts), aetiopathogenesis (immune-mediated, toxic and drug-induced, ischaemic, infectious, genetically determined, or neoplastic), and the predominant morphological pattern of injury (inflammatory or non-inflammatory). While abnormalities in medium-sized and large bile ducts are typically detected using radiological methods, the diagnosis of small duct cholangiopathies continues to rely primarily on histopathological evaluation of liver tissue. This review summarises the key morphological features of the most clinically significant cholangiopathies, focusing on histopathological changes observed in liver biopsy.

Using molecular methods, infectious organisms of viral, bacterial and fungal origin, as well as protozoa and helminths, can be detected. Molecular methods detect specific segments in the nucleic acid sequences of infectious agents and therefore do not require the maintenance of viability of the microorganisms of interest. Therefore, these methods can also be used for direct detection of infectious agents from fixed tissue, the most commonly available material in pathology. This short review article is based on more than 20 years of molecular microbiology within pathology and our aim is to present the possibilities of molecular detection of infectious organisms for pathological diagnosis.

With the advancing digitalization of pathology, the application of machine learning and artificial intelligence methods is becoming increasingly important. Research and development in this field are progressing rapidly, but the clinical implementation of learning systems still lags behind. The aim of this text is to provide an overview of the process of developing and deploying learning systems in digital pathology. We begin by describing the fundamental characteristics of data produced in digital pathology. Specifically, we discuss scanners and sample scanning, data storage and transmission, quality control, and preparation for processing by learning systems, with a particular focus on annotations. Our goal is to present current approaches to addressing technical challenges while also highlighting potential pitfalls in processing digital pathology data. In the first part of the text, we also outline existing software solutions for viewing scanned samples and implementing diagnostic procedures that incorporate learning systems. In the second part of the text, we describe common tasks in digital pathology and outline typical approaches to solving them. Here, we explain the necessary modifications to standard machine learning methods for processing large scans and discuss specific diagnostic applications. Finally, we provide a brief overview of the potential future development of learning systems in digital pathology. We illustrate the transition to large foundational models and introduce the topic of virtual staining of samples. We hope that this text will contribute to a better understanding of the rapidly evolving field of machine learning in digital pathology and, in turn, facilitate the faster adoption of learning-based methods in this domain.

This case report describes a 71-year-old male patient in whom a solitary fibrous tumor (SFT) of the pancreatic tail was incidentally discovered during staging of chronic lymphocytic leukemia/small lymphocytic lymphoma (B-CLL/SLL). The patient also had a history of excised malignant melanoma. SFT is a mesenchymal neoplasm characterized by NAB2::STAT6 gene fusion, STAT6 and CD34 immunohistochemical positivity, and unclear biological behavior. In this case, the tumor was a firm, well-circumscribed spindle cell lesion without cytologic atypia, necrosis, or significant mitotic activity, showing strong diffuse STAT6 and CD34 expression. According to WHO classification criteria, it was classified as a low-risk SFT with respect to metastatic potential. The diagnosis of SFT is based on characteristic morphology and nuclear expression of STAT6, which helps distinguish it from a broad spectrum of CD34-positive mesenchymal lesions. The article discusses relevant differential diagnoses and highlights the molecular basis of SFT, including the prognostic implications of different NAB2::STAT6 fusion variants and the association of TERT promoter mutations with more aggressive behavior. Although pancreatic SFT is rare, similar cases have been reported in the literature. From a clinical standpoint, accurate risk stratification for recurrence or metastasis is essential. Several scoring systems have been proposed and validated, including the one adopted in the WHO classification, which considers tumor size, mitotic rate, necrosis, and patient age. In this case, the tumor was completely resected, and the patient has remained disease-free with no signs of SFT recurrence or B-CLL/SLL progression more than six months after surgery.

Cytological examination of extrahepatic bile ducts represents a challenging diagnostic field, often limited by sample quality and cellularity. Despite the availability of detailed classifications and cellular morphology descriptions, distinguishing benign from malignant lesions remains difficult in clinical practice. This paper reviews the current WHO classification system for pancreatobiliary cytopathology, with a focus on diagnostic categories specific to extrahepatic bile ducts. It discusses typical cytomorphological features, differential diagnoses, and the use of ancillary techniques such as immunohistochemistry and FISH, which may enhance diagnostic sensitivity and specificity. The importance of clinical context and interdisciplinary collaboration in cytological interpretation is emphasized.