Adrenal gland cytology reporting: a multi‐institutional proposal for a standardized reporting system
Bibliographic record
Abstract
BACKGROUND: With the development of new technologies and the changing patient profiles, cytopathology departments receive increasing numbers of adrenal gland cytology specimens. In this study, the authors analyzed archival adrenal gland cytology cases and attempted to implement a diagnostic reporting system. DESIGN: Retrospective electronic medical record search was performed for adrenal gland cytology specimens in seven tertiary care centers. The cytology diagnoses were grouped in 7 categories: nondiagnostic, nonneoplastic, benign adrenal cortical elements (BACE), primary neoplasm of noncortical origin (NONC), atypia of undetermined significance (AUS), suspicious for malignancy (SM), and malignant (MAL). If available, histopathology results of concurrent and/or follow-up biopsies and/or resections were documented. RESULTS: A total of 473 adrenal gland cytology cases were included. BACE cases comprised 21.8%, whereas MAL cases were 57.5% of all cases. For BACE and MAL categories, there were 100% and 98.9% correlation, respectively, in the cases with histopathology follow-up. Six of 10 NONC cases had histopathology diagnoses and there were 3 pheochromocytomas and 3 schwannomas. Twenty-one AUS cases had histology follow-up and 10 (47.6%) of them were malignant. Six cases of SM had histopathology follow-up, and all of them were malignant on the follow-up. CONCLUSIONS: The authors propose a 7-tier diagnostic scheme for adrenal gland cytology. The risk of malignancy was 98.9% in MAL cases (87/88) in the cohort. The only case with discordance was reported as "adrenal cortical adenoma with marked atypia"' on resection. There was no difference between endoscopic ultrasound-guided and percutaneous methods. Further studies are needed to validate and make this approach universal.
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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 | 0.260 | 0.192 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.015 | 0.008 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.010 | 0.009 |
| Research integrity | 0.004 | 0.006 |
| 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".