European Journal of Molecular & Clinical Medicine

Abstract

Background: The well-established method of fine-needle aspiration cytology (FNAC) presents certain difficulties in its widespread application due to the heterogeneity and morphological overlap of the spectrum of salivary gland disorders. The recent "Milan system for reporting salivary gland cytopathology" (MSRSGC), offers guidelines for diagnosis and treatment based on various categories of malignancy risk (ROM). The goal of this study is to stratify the lesions by assigning the risk of malignancy based on pattern, interpret salivary gland lesions based on MSRSGC, and record interobserver variability.
Methods: FNAC was performed routinely on all palpable salivary gland lesions confirmed by prior USG. FNAC was done under aseptic conditions using 22–24 gauge needles, via percutaneous route either by non-guided or guided aspiration. A minimum of 2 passes were made by trained residents in the department of Pathology. The cytosmears are fixed in 95% ethyl alcohol or air dried and are routinely stained by Hematoxylin and Eosin and PAP stain where ever needed. Each case was evaluated for adequacy and categorized into one of the six categories according to MSRSGC by two different faculty members and tabulated. These cases were assigned to categories based on established criteria in the literature.
Results: Most commonly categorized category is Category-IVa. Benign neoplasm (68/136) in this Pleomorphic adenoma (56/68) is the commonest diagnosis.  In Non-Neoplastic; chronic sialadenitis (24/40) was the most common diagnosis. We have received 49 cases for histopathological examination. In cyto-histopathological correlation, concordance was seen with 39/49 cases. 10/49 cases were found to be discordant out of them 4/10 were benign and 6/10 were malignant.
Conclusion: the use of the Milan System of Reporting Salivary Gland Cytopathology in regular cytology reporting to improve the communication between pathologists and also with the clinician for better patient care. The use of pattern-based categorization could help in complimenting MSRSGC, as it adds to the risk stratification. as it has a higher positive predictive value.