Application of the Milan System for Reporting Submandibular Gland Cytopathology: An international, multi‐institutional study
Bibliographic record
Abstract
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".