ReviewActa cytologica2026
Pulmonary Cytology in Transformation.
Review in Acta cytologica, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundTraditionally, pulmonary cytology has been used to diagnose and screen for a variety of respiratory diseases, including lung cancer. Owing to its rapid turnaround time compared with histological diagnosis, therapeutic decisions may occasionally need to be made based on cytological findings alone. In the era of precision medicine, however, biomarker assessment and molecular testing have become increasingly important, particularly in the management of lung cancer. In addition, the digitalization of pathological slides has facilitated the development of artificial intelligence (AI)-based diagnostic tools. The utility of these emerging technologies in pulmonary cytology is currently being actively investigated and continues to evolve, overcoming challenges including low input of tumor cells and the three-dimensional nature of cytological specimens. SUMMARY: This review summarizes recent advances in pulmonary cytology, focusing on immunocytochemistry (ICC), proteomic analysis, and AI-assisted diagnostics. ICC provides crucial information not only for subclassification but also for the assessment of relevant therapeutic biomarkers. Proteomic analysis enables comprehensive characterization of thousands of protein expression profiles simultaneously, using limited cytology materials. But its future use in routine diagnostics must be established and tested in clinical trials. Furthermore, digitalized cytological slides can be utilized for AI-based analyses, potentially reducing diagnostic workload and providing additional predictive information. Despite these advances, challenges remain regarding specimen preparation, standardization, and validation. KEY MESSAGES: The integration of morphology with appropriately validated molecular and computational methods has the potential to transform the framework of pulmonary cytology, extending its role from morphological diagnosis toward comprehensive and clinically significant characterization of pulmonary tumors.
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