TITLE:
Morphometric and Distributional Analysis of Nuclear Features in Oral Exfoliative Cytology: Insights from Cytological Classification
AUTHORS:
Masaaki Suemitsu, Mariko Hata, Atsushi Tsurumi, Mitsuko Nakayama, Tadahiko Utsunomiya, Kayo Kuyama
KEYWORDS:
Mouth Neoplasms, Exfoliative Cytology, Morphometry Cell Nucleus, Hyperchromasia, Image Processing
JOURNAL NAME:
Open Journal of Stomatology,
Vol.15 No.11,
November
13,
2025
ABSTRACT: Background: Oral squamous cell carcinoma (OSCC) often arises from precancerous lesions and is frequently asymptomatic in its early stages. Oral exfoliative cytology is a minimally invasive screening tool for early detection, but diagnostic accuracy remains suboptimal due to the subjective interpretation of cytological findings. Objective and quantitative evaluation of nuclear morphology may enhance diagnostic precision. Methods: A total of 9593 cytological images of squamous epithelial cells were analyzed from 85 cases, categorized into five diagnostic groups: negative for intraepithelial lesion or malignancy (NILM) without inflammation (NILM [inf−]), NILM with inflammation (NILM [inf+]), oral low-grade squamous intraepithelial lesion (OLSIL), oral high-grade squamous intraepithelial lesion (OHSIL), and OSCC. Nuclear area and hyperchromasia were quantified using image analysis software. Statistical analyses and kernel density estimation were performed to evaluate intergroup differences and distributions. Results: The OSCC group exhibited the largest nuclear area and the greatest hyperchromasia, with the broadest distribution of nuclear features. The OHSIL group also showed significant nuclear enlargement and heterogeneity. Positive correlations between nuclear area and hyperchromasia were observed in non-neoplastic groups, but they were weakened in the OHSIL and OSCC groups, reflecting tumor-associated nuclear pleomorphism. Most pairwise comparisons showed significant differences in nuclear features between groups. Conclusion: Quantitative assessment of nuclear area and hyperchromasia distinguishes between diagnostic categories in oral cytology. These findings suggest that morphometric image analysis may improve diagnostic accuracy and aid in the early detection and risk stratification of OSCC.