Abstract

Introduction: Fine Needle Aspiration Cytology (FNAC) is one of the commonest and most efficient investigation done for salivary gland swellings, which is of much help in diagnosing inflammatory, benign and malignant lesions. The morphology of nucleus determines the behaviour of the cell/tissue as benign or malignant. Nuclear morphometry analysis is done by using various software. Aim: To determine the nuclear morphometry parameters in salivary gland neoplasm of FNAC smears by Image J software and to determine its association with the cytological diagnosis. Materials and Methods: This was a retrospective laboratory observational cross-sectional study done for four years (2018- 2021) at a tertiary healthcare centre, attached to Sri DevarajUrs Medical College, Kolar, Karnataka, India. All the FNAC smears of salivary gland neoplasms were considered and classified by cytomorphology as benign and malignant tumours. All smears were analysed using Image J software for nuclear morphometric parameters such as area of the nucleus, perimeter of the nucleus, feret diameter, minimum ferret and skewness. The nuclear morphometric findings of benign and malignant tumours were compared and statistical analysis was doneby using Student’s t-test, p-value of <0.05 was considered statistically significant. Results: A total of 52 cases were studied. The average age of presentation of benign and malignant salivary gland neoplasm was 38.7 and 49.4 years, respectively. Among, 30 (57.6%) benign neoplasms there were 23 (76.7%), pleomorphic adenoma was the commonest and among 22 (42.4%) malignant squamous cell carcinoma 16 (72.7%) deposits was the commonest. The nuclear morphometry analysis showed that the mean values of area (p-value ≤0.001), perimeter (p-value≤0.001), ferret diameter (p-value≤0.001) and minimum ferret (p-value=0.001) of malignant lesions were comparatively higher than benign and was statistically significant. Conclusion: Nuclear morphometry along with routine cytopathological evaluation will improve the accuracy of diagnosis of neoplastic lesions of salivary gland. This information can be used to plan better treatment and predict prognosis of the disease. Digital morphological analysis helps in obtaining quantitative values from qualitative data which can be utilised further for automation.

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