A Deep Learning–Enabled Single-Cell Morpholomic Atlas of Nasal Swabs Distinguishes Chronic Inflammation from Sinonasal Malignancy

ABSTRACT Background: Sinonasal malignancies frequently present with symptoms overlapping chronic inflammatory conditions such as chronic rhinosinusitis (CRS), complicating early detection and delaying treatment. A fast, scalable, non-invasive approach capable of resolving immune and epithelial cell states across inflammatory and malignant disease from routine nasal swabs could substantially improve clinical screening, leading to the initiation of appropriate treatment. Methods: We developed a deep learning–enabled single-cell morpholomic framework using the REM-I platform to generate a reference atlas of >641K cell brightfield images from purified immune cell populations. This reference atlas was applied to >2.5 million images obtained from nasal swabs spanning a clinical spectrum of health, CRS, and sinonasal carcinoma. Embeddings were integrated using dimensionality reduction for differential feature testing and comparative feature enrichment across disease states. Findings Across the disease continuum, sinonasal carcinoma samples exhibited distinct immune remodeling, including increased myeloid-like cell abundance and elevated small dark pixel intensity consistent with enhanced granulocyte activity. Basophil/NK-enriched clusters contained tumor-associated cells with deep learning–derived morphologic signatures not observed in CRS or healthy samples. Tumor-associated epithelial cells were significantly smaller and displayed disease-specific morpholomic patterns distinct from chronic inflammation. Conclusions This study establishes a deep learning–enabled single-cell morpholomic atlas of nasal swabs spanning healthy epithelium, chronic inflammation and sinonasal malignancies. Morpholomic cytology reveals reproducible immune and epithelial states associated with inflammatory and malignant disease and provides a scalable, non-invasive framework for cellular stratification in sinonasal pathology, supporting future applications in early point-of-care diagnostics.