Journal of Cytology & Histology

Histopathology has long been a cornerstone of disease diagnosis and prognosis, enabling healthcare professionals to examine tissue samples for abnormalities and pathological changes. In recent years, the field of histopathology has seen remarkable advancements, driven by innovative techniques and technologies. This article explores emerging trends in histopathology techniques for disease detection, covering topics such as digital pathology, Artificial Intelligence (AI) integration, multiplexed assays, and tissue engineering. These trends are reshaping the landscape of histopathology, offering improved accuracy, efficiency, and diagnostic insights, ultimately leading to better patient care.

Aims: Lymphatic invasion by D2-40 immunostaining has been evaluated based on the presence of carcinoma cells in the lymphatic lumen, but the entering of carcinoma cells with an invading style into the lymphatic lumen of a lymphatic vessel was reported in early gastric adenocarcinoma (EGAC), in which carcinoma cells localize within mucosa or submucosa. Here, a detailed examination of lymphatic invasion by D2-40 immunostaining is made in EGAC.

Methods: A total of 204 EGAC patients who underwent endoscopic submucosal dissection was examined. Lymphatic invasion was classified as intra-lumen type and invading type by D2-40 immunostaining. In the intra-lumen type, carcinoma cells are present in the lymphatic lumen. In the invading type, carcinoma cells invade the lymphatic vessel with destruction of the lymphatic wall and enter the lymphatic lumen.

Results: Lymphatic invasion was noted in 15 cases. The sites of the invasion were mucosa (1 case), mucosa and submucosa (7 cases), and submucosa (7 cases). Intra-lumen-type invasion was present in all 15 cases, and invading-type invasion was noted in 2 cases (mucosa in 1 case and mucosa and submucosa in 1 case). These data indicate that carcinoma cells entered from both mucosal and submucosal lymphatic vessels and carcinoma cells in the mucosal lymphatic vessels remained in the mucosal lymphatic vessels or moved to submucosal lymphatic vessels. The carcinoma cells in the lymphatic vessels of the submucosa consisted of carcinoma cells that moved from the mucosal lymphatic vessels and carcinoma cells entered from submucosal lymphatic vessels, or they consisted of carcinoma cells that entered from submucosal lymphatic vessels only.

Conclusion: The classification of lymphatic invasion consisting of intra-lumen type and invading type offers an increase in diagnostic accuracy of lymphatic invasion, and it clarifies the entry and movement system of carcinoma cells in lymphatic vessels.

Histopathological examination is a fundamental tool in the diagnosis and prognosis of various diseases, including cancer. Traditional histopathology relies on the visual analysis of tissue samples, often accompanied by stains that highlight specific features. However, this method has limitations, especially when distinguishing between different tissue types and subtypes. Immunohistochemistry (IHC) has revolutionized histopathology by introducing the use of antibodies to detect specific proteins within tissues. This article explores the significant impact of immunohistochemistry in histopathological diagnosis, discussing its principles, applications, advantages, and challenges. Immunohistochemistry, histopathological diagnosis, antibodies, tissue samples, cancer, proteins. Histopathological diagnosis plays a crucial role in the assessment and management of various diseases, including cancer. Traditionally, pathologists relied on Hematoxylin and Eosin (H&E) staining to visualize tissue samples, but this method often had limitations, especially when differentiating between various tissue types and subtypes.

Histopathology plays a pivotal role in the era of personalized medicine, where treatment strategies are increasingly tailored to individual patients. This article explores the convergence of histopathology and personalized medicine, highlighting the significance of histopathological analysis in guiding patient-specific treatment plans. We delve into the evolving landscape of personalized medicine and the diverse techniques and technologies that are revolutionizing the field of histopathology. Histopathology, Personalized medicine, Precision medicine, Treatment strategies, Biomarkers, Molecular diagnostics. In some cases, obtaining adequate tissue samples for histopathological analysis can be challenging, especially in metastatic or recurrent cancers. Minimally invasive techniques and liquid biopsies are being developed to overcome this limitation.

Histopathology is a cornerstone of modern medicine, enabling the diagnosis and understanding of various diseases at a microscopic level. In recent years, Artificial Intelligence (AI) has emerged as a transformative tool in histopathology, offering new capabilities and efficiencies in disease detection, classification, and prognosis. This article provides a comprehensive overview of the role of AI in histopathology, discussing its applications, challenges, and potential future directions. It covers topics such as image analysis, machine learning, deep learning, and the ethical considerations surrounding AI implementation. This article provides a comprehensive overview of the role of AI in histopathology. It will cover the various applications, challenges, and future prospects of AI in this field, including image analysis, machine learning, deep learning, and ethical considerations.