Application of the Milan System for Reporting Submandibular Gland Cytopathology: An international, multi‐institutional study
Background The Milan System for Reporting Salivary Gland Cytopathology (MSRSGC) is a 6‐tier diagnostic category system with associated risks of malignancy (ROMs) and management recommendations. Submandibular gland fine‐needle aspiration (FNA) is uncommon with a higher frequency of inflammatory lesio...
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| Published in: | Cancer cytopathology Vol. 127; no. 5; pp. 306 - 315 |
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| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
United States
Wiley Subscription Services, Inc
01-05-2019
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| Subjects: | |
| Online Access: | Get full text |
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| Summary: | Background
The Milan System for Reporting Salivary Gland Cytopathology (MSRSGC) is a 6‐tier diagnostic category system with associated risks of malignancy (ROMs) and management recommendations. Submandibular gland fine‐needle aspiration (FNA) is uncommon with a higher frequency of inflammatory lesions and a higher relative proportion of malignancy, and this may affect the ROM and subsequent management. This study evaluated the application of the MSRSGC and the ROM for each diagnostic category for 734 submandibular gland FNAs.
Methods
Submandibular gland FNA cytology specimens from 15 international institutions (2013‐2017) were retrospectively assigned to an MSRSGC diagnostic category as follows: nondiagnostic, nonneoplastic, atypia of undetermined significance (AUS), benign neoplasm, salivary gland neoplasm of uncertain malignant potential (SUMP), suspicious for malignancy (SM), or malignant. A correlation with the available histopathologic follow‐up was performed, and the ROM was calculated for each MSRSGC diagnostic category.
Results
The case cohort of 734 aspirates was reclassified according to the MSRSGC as follows: nondiagnostic, 21.4% (0%‐50%); nonneoplastic, 24.2% (9.1%‐53.6%); AUS, 6.7% (0%‐14.3%); benign neoplasm, 18.3% (0%‐52.5%); SUMP, 12% (0%‐37.7%); SM, 3.5% (0%‐12.5%); and malignant, 13.9% (2%‐31.3%). The histopathologic follow‐up was available for 333 cases (45.4%). The ROMs were as follows: nondiagnostic, 10.6%; nonneoplastic, 7.5%; AUS, 27.6%; benign neoplasm, 3.2%; SUMP, 41.9%; SM, 82.3%; and malignant, 93.6%.
Conclusions
This multi‐institutional study shows that the ROM of each MSRSGC category for submandibular gland FNA is similar to that reported for parotid gland FNA, although the reported rates for the different MSRSGC categories were variable across institutions. Thus, the MSRSGC can be reliably applied to submandibular gland FNA.
In this retrospective, international, multi‐institutional study, the Milan System for Reporting Salivary Gland Cytopathology (MSRSGC) has been applied to a cohort of 734 submandibular fine‐needle aspirates, and the risk of malignancy (ROM) has been calculated for each diagnostic category. The aspirates have been reclassified as follows: nondiagnostic, 21.4% (0%‐50%); nonneoplastic, 24.2% (9.1%‐53.6%); atypia of undetermined significance (AUS), 6.7% (0%‐14.3%); benign neoplasm, 18.3% (0%‐52.5%); salivary gland neoplasm of uncertain malignant potential (SUMP), 12% (0%‐37.7%); suspicious for malignancy (SM), 3.5% (0%‐12.5%); and malignant, 13.9% (2%‐31.3%). The histopathologic follow‐up is available for 333 cases (45.4%). The ROMs are as follows: nondiagnostic, 10.6%; nonneoplastic, 7.5%; AUS, 27.6%; benign neoplasm, 3.2%; SUMP, 41.9%; SM, 82.3%; and malignant, 93.6%. This study confirms that the MSRSGC can be reliably applied in reporting submandibular gland fine‐needle aspiration. |
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| Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 AUTHOR CONTRIBUTIONS Zahra Maleki: Conceptualization, data curation, formal analysis, investigation, methodology, project administration, resources, software, supervision, validation, visualization, writing–original draft, and writing–review and editing. Zubair Baloch: Data curation, formal analysis, investigation, methodology, resources, validation, visualization, writing–original draft, and writing–review and editing. Ryan Lu: Data curation, investigation, and resources. Khurram Shafique: Data curation, investigation, and resources. Sharon J. Song: Data curation, investigation, and resources. Kartik Viswanathan: Data curation, investigation, and resources. Rema A. Rao: Data curation, investigation, and resources. Holly Lefler: Data curation, investigation, and resources. Aisha Fatima: Data curation, investigation, and resources. Austin Wiles: Data curation, formal analysis, investigation, and resources. Vickie Y. Jo: Data curation, formal analysis, investigation, resources, and writing–review and editing. He Wang: Data curation, formal analysis, investigation, and resources. Guido Fadda: Data curation, formal analysis, investigation, resources, and writing–review and editing. Celeste N. Powers: Data curation, formal analysis, investigation, resources, validation, and writing–review and editing. Syed Z. Ali: Formal analysis, investigation, methodology, and resources. Liron Pantanowitz: Data curation, formal analysis, investigation, resources, and writing–review and editing. Momin T. Siddiqui: Data curation, formal analysis, investigation, resources, and writing–review and editing. Ritu Nayar: Data curation, formal analysis, investigation, resources, software, validation, and writing–review and editing. Jerzy Klijanienko: Data curation, formal analysis, investigation, methodology, resources, and writing–review and editing. Guliz A. Barkan: Data curation, formal analysis, investigation, methodology, resources, visualization, and writing–review and editing. Jeffrey F. Krane: Data curation, formal analysis, investigation, resources, and writing–review and editing. Esther D. Rossi: Conceptualization, data curation, formal analysis, investigation, methodology, resources, and writing–review and editing. Fabiano Callegari: Data curation, formal analysis, investigation, resources, and writing–review and editing. Ivana Kholová: Data curation, formal analysis, investigation, methodology, visualization, resources, and writing–review and editing. Massimo Bongiovanni: Data curation, formal analysis, investigation, resources, and writing–review and editing. William C. Faquin: Data curation, formal analysis, investigation, methodology, resources, validation, and writing–review and editing. Marc P. Pusztaszeri: Conceptualization, data curation, formal analysis, investigation, methodology, project administration, resources, software, supervision, validation, visualization, writing–original draft, and writing–review and editing. |
| ISSN: | 1934-662X 1934-6638 |
| DOI: | 10.1002/cncy.22135 |