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UF-5000 Atyp.C parameter for urinary screening: Impact of detecting atypical and inclusion-bearing cells
Yuki Fujiwara1, Yuzuru Takei2, Hiroto Nakazawa2
1Department of Integrative Physiology, Graduate School of Medicine, Gunma University, Maebashi, Japan.
Abstract:
Urine sediment examination is vital for detecting atypical urothelial cells but is highly operator-dependent. The UF-5000 automated urine analyzer quantifies nucleic acid-containing particles. The atypical cell (Atyp.C) parameter reflects increased nucleic acid content and detects atypical urothelial cells and intracytoplasmic inclusion-bearing (ICIB) cells linked to viral infection or inflammation. We assessed the relationship between Atyp.C, atypical cells, and ICIBs and evaluated the parameter's screening performance. Overall, 264 urine sediment samples from 203 patients were analyzed using the UF-5000. Manual microscopy was used to identify atypical and ICIB-positive cells. Atyp.C values were compared between groups using the Mann-Whitney U test. Diagnostic performance was assessed using receiver operating characteristic (ROC) analysis and sensitivity, specificity, and Cohen's κ calculation at cutoffs of 0.1, 0.3, and 0.5 cells/μL. Atyp.C values were significantly higher in atypical-cell-positive specimens (p < 0.001). Atypical cell and ICIB positivity showed moderate agreement (Cohen's κ = 0.534; 77 % agreement; p < 0.01). ROC analysis showed an area under the curve of 0.829 for atypical cells, which increased to 0.895 when ICIB-positive samples were considered positive. At a cutoff of 0.1 cells/μL, Atyp.C exhibited high sensitivity (97.8 %) with low specificity (46.5 %) for detecting atypical cells; a 0.3 cells/μL cutoff provided optimal balance (sensitivity 85.2 %, specificity 68.2 %). The UF-5000 Atyp.C parameter effectively detects cells with increased nucleic acid content, including atypical and ICIB-positive cells. Recognizing ICIBs as diagnostically relevant improves screening sensitivity, supporting Atyp.C as a valuable tool for urinalysis. Combining automated detection with manual microscopy may improve the efficiency of atypical cell detection.
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