Yuan Zhang, Luo Zhang
Nasal cytology (NC) has reemerged in precision rhinology as a key diagnostic tool that bridges microscopic cellular patterns with macroscopic clinical decisions, providing a unique, real-time, minimally invasive, and cost-effective approach. Different types of rhinitis show distinct NC manifestations. In bacterial rhinitis, NC reveals dense neutrophil infiltration, often with intracellular and extracellular bacteria; in allergic rhinitis (AR), it shows abundant eosinophilic infiltration and mast cells [1,2]. However, the true strength of NC lies in its ability to transcend traditional symptom-based classifications by identifying specific cellular profiles—such as eosinophils, mast cells, and neutrophils—thereby enabling endotype-driven and personalized treatment strategies [3,4]. In chronic rhinosinusitis with nasal polyps (CRSwNP) management, it identifies the eosinophil-mast cell inflammatory endotype, which is closely associated with disease severity and recurrence risk [5]. The development of the Clinical-Cytological Grading and the Prognostic Index of Relapse allows for risk stratification based on cytological findings, guiding targeted therapeutic strategies, including the selection of candidates for biologic therapies [3,6]. Furthermore, NC enables dynamic monitoring of treatment response; for instance, cytological changes following dupilumab treatment can precede clinical improvement, offering an objective measure of efficacy [7]. NC enables inflammatory phenotyping of AR, classifying patients into subgroups, which can inform targeted therapeutic strategies [8]. Similarly, it is crucial for subclassifying non-AR (NAR) into distinct entities like NARES (eosinophilic), NARMA (mast cell-rich), NARNE (neutrophilic), and NARESMA (mixed eosinophilic-mast cell), which exhibit different clinical prognoses and treatment responses, thereby enhancing diagnostic precision and personalized management. For example, NARES patients are more prone to developing aspirin-sensitive asthma and nasal polyps, while NARESMA is often associated with more severe disease, including the development of CRSwNP [9,10]. Besides, NC is used not only for diagnosis but also for monitoring treatment response and predicting outcomes. In AR, repeated NC measurements can show reductions in eosinophils after immunotherapy, predicting short-term effectiveness [11,12]. In Gelardi’s latest review, the indispensable role of NC in the diagnosis, classification, treatment selection, and prognostic assessment of upper airway diseases is systematically elaborated, highlighting its potential as a cornerstone of precision medicine [13]. Despite its potential, NC faces challenges that limit its broad use. Inconsistent sampling methods and a lack of agreed-upon cell count thresholds make it difficult to compare results across clinics. The integration of new technologies is opening new avenues for innovation in NC. Techniques such as DeepDOF-SE microscopy, which combines fluorescent staining, deep learning-enhanced imaging, and virtual staining, show promise in overcoming challenges like the irregular surface of nasal secretions [14]. Similarly, immunofluorescence-based cytometry allows precise differentiation of inflammatory cells via antibody labeling [15]. Especially, artificial intelligence (AI) can automate cell counting, reducing human error and enabling faster, more consistent diagnostics. AI further enhances diagnostic precision through automated cell classification and phenotyping, as demonstrated by systems capable of identifying distinct inflammatory endotypes in chronic rhinosinusitis. These advances support the potential for AI-driven, full-slide analysis to enable personalized treatment strategies based on cellular profiles in nasal diseases [16,17]. NC is evolving from a supplementary tool to a central component of precision medicine in nasal diseases. Future clinical use may include real-time phenotyping at diagnosis, personalized treatment selection, and efficacy monitoring—making cellular profiling a guide at every stage of nasal disease management. Acknowledgements This work was supported by grants from the national key R&D program of China (2022YFC2504100). Conflicts of interest The authors have no conflicts of interest. Author contributions Writing: Yuan Zhang, Luo Zhang.