Cytopathology最新文献

Liquid biopsy is transforming cancer diagnostics and management by enabling minimally invasive molecular profiling through blood. Beyond plasma, cytology specimens such as pleural effusions, cerebrospinal fluid and urine offer valuable sources for molecular testing. In this narrative review, we highlight the central role of cytopathology in integrating liquid biopsy into clinical practice, discussing pre-analytical and analytical challenges across different samples and exploring the contribution of artificial intelligence in improving both morphological and molecular interpretation.

A 39-year-old man was diagnosed with acute lymphoblastic leukaemia (B-ALL) with SYNRG::ZNF384 and P2RY8::CRLF2 gene fusions. He was treated according to the GRAALL 2014 and underwent an allogeneic transplantation, achieving complete remission. Twenty-five months later, he developed pancytopenia. The bone marrow examination showed blasts with an immunophenotype consistent with minimally differentiated acute myeloid leukaemia (AML M0). Although the P2RY8::CRLF2 fusion persisted, no molecular evidence of a lineage switch was detected. This rare presentation underlines the diagnostic challenges of acute leukaemias. The patient was finally treated with azacitidine and venetoclax.

Subcutaneous fat necrosis of the newborn (SCFN) is an uncommon skin disorder seen in neonates, typically manifesting as subcutaneous nodules or plaques within the first few weeks after birth, often following a difficult delivery. The condition is associated with hypercalcaemia, which can present with symptoms such as lethargy, irritability, hypotonia and dehydration, sometimes resembling sepsis. Fine-needle aspiration cytology typically reveals adipocytes and needle-shaped crystals while showing little to no fat necrosis, inflammatory infiltrate or multinucleated giant cells. The diagnosis is confirmed by histopathology, which shows distinctive features of lobular panniculitis, mixed inflammatory cell infiltration and crystals arranged in a radial pattern. It is important to distinguish SCFN from other causes of lobular panniculitis, as early diagnosis and intervention are crucial to prevent potential long-term complications.

We describe a case of leukaemic pleural effusion in a patient with acute myeloid leukaemia and marked marrow fibrosis. Cytologic evaluation of the pleural fluid revealed numerous large atypical cells, morphologically consistent with dysplastic megakaryocytes. This case underscores an important diagnostic pitfall, as such findings may be misinterpreted if the possibility of leukaemic infiltration is not considered. It highlights the critical importance of recognising key cytomorphologic features and integrating immunophenotyping, clinical history and radiologic findings to arrive at an accurate diagnosis. Awareness of this potential presentation is essential to avoid misclassification and ensure appropriate clinical management.

Historically, gynaecologic cytology, particularly cervical screening through Pap smear tests, has been instrumental in early cancer detection, but not without its challenges. These include variability in interpretation and the labour-intensive nature of manual screening processes. The advent of artificial intelligence (AI) technologies, especially machine learning and deep learning, heralds a new era in cytology, offering enhanced accuracy, consistency, and efficiency. These advancements promise to mitigate traditional limitations by automating routine analyses, aiding early cancer detection, and reducing the workload of laboratory personnel. This review thoroughly examines the current status of commercial AI software in gynaecologic cytology screening. We critically assess the capabilities, performance, and impact of these AI tools in a clinical context. Additionally, the review addresses the integration challenges and potential of AI in clinical practice, including workflow integration, regulatory compliance, and ethical considerations. Through this comprehensive analysis, we aim to provide insights into how AI is reshaping gynaecologic cytology, paving the way for more effective disease management and enhanced patient care in women's health.

Evidence-based medicine (EBM) emerged in the mid-1990s as a systematic and data-driven approach to clinical practice. It plays a crucial role in cytopathology, which focuses on identifying both neoplastic and non-neoplastic disorders. Despite its importance, cytopathology faces significant challenges in implementing EBM principles. These include diagnostic variability, interobserver discrepancies, and subjective interpretations, which can impact diagnostic consistency and patient outcomes. These issues often lead to inconsistencies in diagnosis, underdiagnosis, or overdiagnosis, ultimately compromising patient outcomes. To address these challenges, evidence-based cytopathology (EBC) emphasises the critical evaluation of diagnostic tests and the utilisation of well-designed and high-quality data to establish robust guidelines. EBC promotes the adoption of standardised diagnostic criteria, integrates robust study designs with the implementation of advanced technologies like artificial intelligence (AI), and leverages clinical decision support systems (CDSS) to enhance diagnostic accuracy and consistency. However, realising the full benefits of EBC requires collaborative research, multicenter trials, and continuous updates to guidelines. In this paper, we discuss the future efforts in improving EBC outcomes, with a focus on developing precise diagnostic instruments, refining methodologies, and exploring machine learning applications to improve diagnostic reliability, enhance patient care, and support informed clinical decision-making.