Cytologic grading of primary malignant salivary gland tumors: A blinded review by an international panel
Bibliographic record
Abstract
BACKGROUND: Fine needle aspiration (FNA) is commonly used for the preoperative evaluation of salivary gland tumors. Tumor grade is a key factor influencing clinical management of salivary gland carcinomas (SGCs). To assess the ability to grade nonbasaloid SGCs in FNA specimens, an international panel of cytopathologists convened to review and score SGC cases. METHODS: The study cohort included 61 cases of primary SGC from the pathology archives of 3 tertiary medical centers. Cases from 2005 to 2016 were selected, scanned, and digitized. Nineteen cytopathologists blinded to the histologic diagnosis reviewed the digitized cytology slides and graded them as low, high, or indeterminate. The panelists' results were then compared to the tumor grades based on histopathologic examination of the corresponding resection specimens. RESULTS: All but 2 of the 19 (89.5%) expert panelists review more than 20 salivary gland FNAs per year; 16 (84.2%) of the panelists work at academic medical centers, and 13 (68.4%) have more than 10 years' experience. Participants had an overall accuracy of 89.4% in the grading of SGC cases, with 90.2% and 88.3% for low- and high-grade SGC, respectively. Acinic cell carcinoma and mucoepidermoid carcinoma had the highest degree of accuracy, while epithelial-myoepithelial carcinoma and salivary duct carcinoma had the lowest degree of accuracy. As expected, the intermediate-grade SGC cases showed the greatest variability (high-grade, 42.1%; low-grade, 37.5%, indeterminate, 20.4%). CONCLUSION: This study confirms the high accuracy of cytomorphologic grading of primary SGC by FNA as low- or high-grade. However, caution should be exercised when a grade cannot be confidently assigned.
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 | 0.041 | 0.060 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".