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A 4-Year Retrospective Analysis of Salivary Gland Cytopathology Using the Milan System for Reporting Salivary Gland Cytology and Ancillary Studies
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A 4-Year Retrospective Analysis of Salivary Gland Cytopathology Using the Milan System for Reporting Salivary Gland Cytology and Ancillary Studies
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A 4-Year Retrospective Analysis of Salivary Gland Cytopathology Using the Milan System for Reporting Salivary Gland Cytology and Ancillary Studies
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Journal Article
A 4-Year Retrospective Analysis of Salivary Gland Cytopathology Using the Milan System for Reporting Salivary Gland Cytology and Ancillary Studies
2019
Overview
The cytopathology of salivary glands presents major challenges due to the heterogeneity of benign and malignant neoplasms, which is reflected in the large range of WHO 2017 Classifications. Fine needle aspiration (FNA) of salivary gland tumours is still the favoured initial approach as it results in good sensitivity and specificity. The Milan System for Reporting Salivary Gland Cytopathology (MSRSGC) was published in 2018 and comprises seven categories. We report results from a 4-year retrospective analysis of 328 salivary gland FNAs which were reviewed and classified according to the MSRSGC. We assess the risk of neoplasm, the risk of malignancy and the contribution of ancillary studies to the diagnosis. Benign neoplasms were the most frequent diagnosis (44.2%). Malignant and suspicious for malignancy were identified in 11.3% and 4.9% of diagnosed cases, respectively. Histopathological analysis after surgery was available for 216 (65.8%) of the cases. All malignant cases were confirmed post-surgery, and 68.8% of suspicious for malignancy were confirmed as malignant tumours. Immunocytochemistry was informative in 72.3% of cases. Immunocytochemistry and FISH provided the definitive diagnosis in 23.7% and 33% of cases, respectively. In conclusion, the MSRSGC is more effective when specific features of neoplasms can be identified. Ancillary studies help to further characterise salivary gland tumours and thereby increase the accuracy of MSRSGC.
